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  • 46501.
    Verhagen, Harko
    et al.
    DSV, Stockholm University.
    Eladhari, Mirjam P
    Högskolan på Gotland, Institutionen för speldesign, teknik och lärande.
    Johansson, Magnus
    DSV, Stockholm University.
    Social believable NPCs: a conceptual model and analysis of current NPC models2011Konferansepaper (Fagfellevurdert)
  • 46502.
    Verhagen, Harko
    et al.
    Stockholm University.
    Eladhari, Mirjam P
    Högskolan på Gotland.
    Johansson, Magnus
    Stockholm University.
    Social believable NPCs: a conceptual model and analysis of current NPC models2011Konferansepaper (Fagfellevurdert)
  • 46503.
    Verhagen, Harko
    et al.
    Stockholms universitet.
    Eladhari, Mirjam Palosaari
    Malta University, Malta.
    Johansson, Magnus
    Stockholms universitet.
    McCoy, Josh
    University of Southern California, USA.
    Social Believability in Games2013Inngår i: Advances in Computer Entertainment: 10th International Conference. Proceedings / [ed] Dennis Reidsma, Haruhiro Katayose, Anton Nijholt, Springer International Publishing , 2013, s. 649-652Konferansepaper (Fagfellevurdert)
    Abstract [en]

    The Social Believability in Games Workshop intends to be a point of interaction for researchers and game developers interested in different aspects of modelling, discussing, and developing believable social agents and Non-Player Characters (NPCs). This can include discussions around behaviour based on social and behavioural science theories and models, social affordances when interacting with game worlds and more. The intention is to invite participants from a multitude of disciplines in order to create a broad spectrum of approaches to the area.

  • 46504.
    Verhagen, Harko
    et al.
    Stockholms universitet, Samhällsvetenskapliga fakulteten, Institutionen för data- och systemvetenskap.
    Elsenbroich, Corinna
    Surrey University, , .
    Computational Social Science and the Search for Mechanisms2013Annet (Annet (populærvitenskap, debatt, mm))
  • 46505.
    Verhagen, Harko
    et al.
    Stockholms universitet, Samhällsvetenskapliga fakulteten, Institutionen för data- och systemvetenskap.
    Elsenbroich, Corinna
    Putting the agent back together again: needs for integrating social and behavioural sciences for agent-based social simulation2012Inngår i: Report from Dagstuhl Seminar 12111: Normative Multi-Agent Systems / [ed] Giulia Andrighetto, Guido Governatori, Pablo Noriega, and Leon van der Torre, 2012, s. 46-Konferansepaper (Annet vitenskapelig)
  • 46506.
    Verhagen, Harko
    et al.
    Stockholms universitet, Samhällsvetenskapliga fakulteten, Institutionen för data- och systemvetenskap.
    Elsenbroich, Corinna
    Fällström, Kurt
    Stockholms universitet, Samhällsvetenskapliga fakulteten, Institutionen för data- och systemvetenskap.
    Modelling Contextual Decision-Making in Dilemma Games2017Inngår i: Advances in Social Simulation 2015 / [ed] Wander Jager, Rineke Verbrugge, Andreas Flache, Gert de Roo, Lex Hoogduin, Charlotte Hemelrijk, Cham, Switzerland: Springer, 2017, s. 121-127Kapittel i bok, del av antologi (Fagfellevurdert)
    Abstract [en]

    Social dilemmas such as the Prisoner’s Dilemma and the Tragedy of the Commons have attracted widespread interest in several social sciences and humanities including economics, sociology and philosophy. Different frameworks of human decision-making produce different answers to these dilemmas. Common for most real-world analyses of the dilemmas is finding that behaviour and choices depend on the decision context. Thus an all-in-one solution such as the rational choice model is untenable. Rather, a framework for agent-based social simulation of real-world behaviour should start by recognising different modes of decisionmaking. This paper presents such a framework and an initial evaluation of its results in two cases, (1) a repeated prisoner’s dilemma tournament playing against a set of well-known base models, and (2) a Tragedy of the Commons simulation.

  • 46507.
    Verhagen, Harko
    et al.
    Stockholms universitet, Institutionen för data- och systemvetenskap.
    Johansson, Magnus
    Stockholms universitet, Institutionen för data- och systemvetenskap.
    Demystifying guilds: MMORPG-playing and norms2009Inngår i: Breaking New Ground: Innovation in Games, Play, Practice and Theory, 2009Konferansepaper (Fagfellevurdert)
  • 46508.
    Verhagen, Harko
    et al.
    Stockholms universitet, Institutionen för data- och systemvetenskap.
    Johansson, Magnus
    Stockholms universitet, Institutionen för data- och systemvetenskap.
    Demystifying guilds: MMORPG-playing and norms2009Inngår i: Breaking New Ground: Innovation in Games, Play, Practice and Theory, 2009Konferansepaper (Fagfellevurdert)
  • 46509.
    Verhagen, Harko
    et al.
    Stockholms universitet, Samhällsvetenskapliga fakulteten, Institutionen för data- och systemvetenskap.
    Johansson, Magnus
    Stockholms universitet, Samhällsvetenskapliga fakulteten, Institutionen för data- och systemvetenskap.
    Demystifying guilds: MMORPG-playing and norms2009Inngår i: Breaking New Ground: Innovation in Games, Play, Practice and Theory, 2009Konferansepaper (Fagfellevurdert)
  • 46510.
    Verhagen, Harko
    et al.
    DSV, Stockholm University.
    Johansson, Magnus
    DSV, Stockholm University.
    Eladhari, Mirjam P
    Högskolan på Gotland, Institutionen för speldesign, teknik och lärande.
    Model of Social Believable NPCs for Teacher Training2011Inngår i: Proceedingsof the 5th European Conference on Games Based Learning / [ed] Dimitris Gouscos and Michalis Meimaris, Athens, Greece: The National and Kapodistrian University of Athens , 2011, s. 771-774Konferansepaper (Fagfellevurdert)
    Abstract [en]

    This paper proposes a conceptual model for non-player characters for use in a serious game application. The game is aimed for teacher training with a focus on training of social skills related to conflict handling. Conflict handling is difficult to emulate in a realistic way and appears not frequent enough in the practical training part of teacher education to enable sufficient training. Also, training in a real world situation may be ethically less sound. To develop a serious game for conflict handling training, we need to create non-player characters that can emulate conflicts in a realistic way. For this, we need to extend current models with social and emotional aspects. We present previously developed meta-models that enable us to propose such a model and combine these and recent game research to a Model Social Game Agent.

  • 46511.
    Verhagen, Harko
    et al.
    Stockholm University.
    Johansson, Magnus
    Stockholm University.
    Eladhari, Mirjam P
    Högskolan på Gotland.
    Model of Social Believable NPCs for Teacher Training2011Inngår i: Proceedings of the 5th European Conference on Games Based Learning / [ed] Dimitris Gouscos and Michalis Meimaris, Athens, Greece: The National and Kapodistrian University of Athens , 2011, s. 771-774Konferansepaper (Fagfellevurdert)
    Abstract [en]

    This paper proposes a conceptual model for non-player characters for use in a serious game application. The game is aimed for teacher training with a focus on training of social skills related to conflict handling. Conflict handling is difficult to emulate in a realistic way and appears not frequent enough in the practical training part of teacher education to enable sufficient training. Also, training in a real world situation may be ethically less sound. To develop a serious game for conflict handling training, we need to create non-player characters that can emulate conflicts in a realistic way. For this, we need to extend current models with social and emotional aspects. We present previously developed meta-models that enable us to propose such a model and combine these and recent game research to a Model Social Game Agent.

  • 46512.
    Verhagen, Harko
    et al.
    Uppsala universitet, Humanistisk-samhällsvetenskapliga vetenskapsområdet, Historisk-filosofiska fakulteten, Institutionen för speldesign. Stockholms universitet, Institutionen för data- och systemvetenskap.
    Johansson, Magnus
    Uppsala universitet, Humanistisk-samhällsvetenskapliga vetenskapsområdet, Historisk-filosofiska fakulteten, Institutionen för speldesign. Stockholms universitet, Institutionen för data- och systemvetenskap.
    Eladhari, Mirjam P.
    The Need for Socially Believable NPCs: Game Designers' View2012Inngår i: ECREA’s Pre-Conference: Experiencing Digital Games: Use, Effects & Culture of Gaming, 2012Konferansepaper (Fagfellevurdert)
  • 46513.
    Verhagen, Harko
    et al.
    Stockholms universitet, Samhällsvetenskapliga fakulteten, Institutionen för data- och systemvetenskap.
    Johansson, Magnus
    Stockholms universitet, Samhällsvetenskapliga fakulteten, Institutionen för data- och systemvetenskap.
    Eladhari, Mirjam P.
    The Need for Socially Believable NPCs: Game Designers' View2012Inngår i: ECREA’s Pre-Conference: Experiencing Digital Games: Use, Effects & Culture of Gaming, 2012Konferansepaper (Fagfellevurdert)
  • 46514.
    Verhagen, Harko
    et al.
    Stockholms universitet.
    Johansson, Magnus
    Stockholms universitet.
    Eladhari, Mirjam P
    The Need for Socially Believable NPCs: Game Designers' View2012Inngår i: ECREA’s Pre-Conference: Experiencing Digital Games: Use, Effects & Culture of Gaming, 2012Konferansepaper (Fagfellevurdert)
  • 46515.
    Verhagen, Harko
    et al.
    Stockholms universitet, Samhällsvetenskapliga fakulteten, Institutionen för data- och systemvetenskap.
    Johansson, Magnus
    Jager, Wander
    Games and Online Research Methods2017Inngår i: The SAGE Handbook of Online Research Methods / [ed] Nigel Fielding, Raymond M. Lee, Grant Blank, Los Angeles: Sage Publications, 2017, nr 2, s. 295-306Kapittel i bok, del av antologi (Fagfellevurdert)
  • 46516.
    Verhagen, Harko
    et al.
    Stockholms universitet, Samhällsvetenskapliga fakulteten, Institutionen för data- och systemvetenskap.
    Noriega, PabloConsejo Superior de Investigaciones Científicas, , Institut d'Investigació en Intelligència Artificial.Balke, TinaSurrey University.De Vos, MarinaBath University .
    Social coordination: principles, artefacts and theories (SOCIAL.PATH).2013Konferanseproceedings (Annet (populærvitenskap, debatt, mm))
  • 46517.
    Verhagen, Harko
    et al.
    Stockholms universitet, Samhällsvetenskapliga fakulteten, Institutionen för data- och systemvetenskap.
    Noriega, Pablo
    d’Inverno, Mark
    Towards a Design Framework for Controlled Hybrid Social Games2013Inngår i: Social Coordination: Principles, Artefacts and Theories (SOCIAL.PATH): AISB Convention 2013 / [ed] Harko Verhagen, Pablo Noriega, Tina Balke, Marina de Vos, 2013, s. 83-87Konferansepaper (Fagfellevurdert)
    Abstract [en]

    We propose a framework for designing and deploying games where social behaviour is kept under control. This framework may also be used for designing other dynamic coordinated social spaces.

  • 46518.
    Verhagen, Harko
    et al.
    Stockholms universitet, Institutionen för data- och systemvetenskap.
    Palosaari Eladhari, Mirjam
    Johansson, Magnus
    Stockholms universitet, Institutionen för data- och systemvetenskap.
    McCoy, Josh
    Social Believability in Games2013Inngår i: Advances in Computer Entertainment: 10th International Conference. Proceedings / [ed] Dennis Reidsma, Haruhiro Katayose, Anton Nijholt, Springer International Publishing , 2013, s. 649-652Konferansepaper (Fagfellevurdert)
    Abstract [en]

    The Social Believability in Games Workshop intends to be a point of interaction for researchers and game developers interested in different aspects of modelling, discussing, and developing believable social agents and Non-Player Characters (NPCs). This can include discussions around behaviour based on social and behavioural science theories and models, social affordances when interacting with game worlds and more. The intention is to invite participants from a multitude of disciplines in order to create a broad spectrum of approaches to the area.

  • 46519.
    Verhagen, Harko
    et al.
    Stockholms universitet, Samhällsvetenskapliga fakulteten, Institutionen för data- och systemvetenskap.
    Palosaari Eladhari, Mirjam
    Johansson, Magnus
    Stockholms universitet, Samhällsvetenskapliga fakulteten, Institutionen för data- och systemvetenskap.
    McCoy, Josh
    Social Believability in Games2013Inngår i: Advances in Computer Entertainment: 10th International Conference. Proceedings / [ed] Dennis Reidsma, Haruhiro Katayose, Anton Nijholt, Springer International Publishing , 2013, s. 649-652Konferansepaper (Fagfellevurdert)
    Abstract [en]

    The Social Believability in Games Workshop intends to be a point of interaction for researchers and game developers interested in different aspects of modelling, discussing, and developing believable social agents and Non-Player Characters (NPCs). This can include discussions around behaviour based on social and behavioural science theories and models, social affordances when interacting with game worlds and more. The intention is to invite participants from a multitude of disciplines in order to create a broad spectrum of approaches to the area.

  • 46520.
    Verhagen, Henricus
    KTH, Tidigare Institutioner, Data- och systemvetenskap, DSV.
    Autonomy and reasoning for natural and artificial agents2004Inngår i: Agents And Computational Autonomy: Potential, Risks, And Solutions / [ed] Nickles, M; Rovatsos, M; Weiss, G, 2004, Vol. 2969, s. 83-94Konferansepaper (Fagfellevurdert)
    Abstract [en]

    In this article, I will present recent thoughts and theories on autonomy for natural and artificial agents. Even though the recent work on autonomy for artificial agents has interesting aspects, it excels in being unsystematic and a lack of references to theories outside of agent research supporting one or the other. Embedding these discussions in a broader framework of discussions in philosophy and sociology will enable us to sketch a broader yet more detailed picture. It will also enable us to discuss the reasoning of artificial agents.

  • 46521.
    Verhagen, Henricus
    Stockholms universitet.
    Norm autonomous agents2000Doktoravhandling, monografi (Annet vitenskapelig)
  • 46522.
    Verhulsdonck, Tijmen
    KTH, Skolan för informations- och kommunikationsteknik (ICT).
    One Shot Object Detection: For Tracking Purposes2017Independent thesis Advanced level (degree of Master (Two Years)), 20 poäng / 30 hpOppgave
    Abstract [en]

    One of the things augmented reality depends on is object tracking, which is a problem classically found in cinematography and security. However, the algorithms designed for the classical application are often too expensive computationally or too complex to run on simpler mobile hardware. One of the methods to do object tracking is with a trained neural network, this has already led to great results but is unfortunately still running into some of the same problems as the classical algorithms. For this reason a neural network designed specifically for object tracking on mobile hardware needs to be developed. This thesis will propose two di erent neural networks designed for object tracking on mobile hardware. Both are based on a siamese network structure and methods to improve their accuracy using filtering are also introduced. The first network is a modified version of “CNN architecture for geometric matching” that utilizes an a ne regression to perform object tracking. This network was shown to underperform in the MOT benchmark as-well as the VOT benchmark and therefore not further developed. The second network is an object detector based on “SqueezeDet” in a siamese network structure utilizing the performance optimized layers of “MobileNets”. The accuracy of the object detector network is shown to be competitive in the VOT benchmark, placing at the 16th place compared to trackers from the 2016 challenge. It was also shown to run in real-time on mobile hardware. Thus the one shot object detection network used for a tracking application can improve the experience of augmented reality applications on mobile hardware. 

  • 46523.
    Verikas, Antanas
    et al.
    Högskolan i Halmstad, Sektionen för Informationsvetenskap, Data– och Elektroteknik (IDE), Halmstad Embedded and Intelligent Systems Research (EIS).
    Bacauskiene, M.
    Department of Applied Electronics, Kaunas University of Technology, Kaunas, Lithuania.
    Using artificial neural networks for process and system modelling2003Inngår i: Chemometrics and Intelligent Laboratory Systems, ISSN 0169-7439, E-ISSN 1873-3239, Vol. 67, nr 2, s. 187-191Artikkel i tidsskrift (Fagfellevurdert)
    Abstract [en]

    This letter concerns several papers, devoted to neural network-based process and system modelling, recently published in the Chemometrics and Intelligent Laboratory Systems journal. Artificial neural networks have proved themselves to be very useful in various modelling applications, because they can represent complex mapping functions and discover the representations using powerful learning algorithms. An optimal set of parameters for defining the functions is learned from examples by minimizing an error functional. In various practical applications, the number of examples available for estimating parameters of the models is rather limited. Moreover, to discover the best model, numerous candidate models must be trained and evaluated. In such thin-data situations, special precautions are to be taken to avoid erroneous conclusions. In this letter, we discuss three important issues, namely network initialization, over-fitting, and model selection, the right consideration of which can be of tremendous help in successful network design and can make neural modelling results more valuable.

  • 46524.
    Verikas, Antanas
    et al.
    Högskolan i Halmstad, Sektionen för Informationsvetenskap, Data– och Elektroteknik (IDE), Halmstad Embedded and Intelligent Systems Research (EIS), Intelligenta system (IS-lab).
    Bacauskiene, M.
    Department of Applied Electronics, Kaunas University of Technology, Kaunas, Lithuania.
    Dosinas, A.
    Department of Applied Electronics, Kaunas University of Technology, Kaunas, Lithuania.
    Bartkevicius, V.
    Department of Applied Electronics, Kaunas University of Technology, Kaunas, Lithuania.
    Gelzinis, A.
    Department of Applied Electronics, Kaunas University of Technology, Kaunas, Lithuania.
    Vaitkunas, M.
    Department of Applied Electronics, Kaunas University of Technology, Kaunas, Lithuania.
    Lipnickas, A.
    Department of Applied Electronics, Kaunas University of Technology, Kaunas, Lithuania.
    An intelligent system for tuning magnetic field of a cathode ray tube deflection yoke2003Inngår i: Knowledge-Based Systems, ISSN 0950-7051, E-ISSN 1872-7409, Vol. 16, nr 3, s. 161-164Artikkel i tidsskrift (Fagfellevurdert)
    Abstract [en]

    This short communication concerns identification of the number of magnetic correction shunts and their positions for deflection yoke tuning to correct the misconvergence of colours of a cathode ray tube. The misconvergence of colours is characterised by the distances measured between the traces of red and blue beams. The method proposed consists of two phases, namely, learning and optimisation. In the learning phase, the radial basis function neural network is trained to learn a mapping: correction shunt position→changes in misconvergence. In the optimisation phase, the trained neural network is used to predict changes in misconvergence depending on a correction shunt position. An optimisation procedure based on the predictions returned by the neural net is then executed in order to find the minimal number of correction shunts needed and their positions. During the experimental investigations, 98% of the deflection yokes analysed have been tuned successfully using the technique proposed.

  • 46525.
    Verikas, Antanas
    et al.
    Högskolan i Halmstad, Sektionen för Informationsvetenskap, Data– och Elektroteknik (IDE), Halmstad Embedded and Intelligent Systems Research (EIS), Intelligenta system (IS-lab).
    Bacauskiene, M.
    Department of Applied Electronics, Kaunas University of Technology Kaunas, Lithuania.
    Malmqvist, Kerstin
    Högskolan i Halmstad, Sektionen för Informationsvetenskap, Data– och Elektroteknik (IDE), Halmstad Embedded and Intelligent Systems Research (EIS), Intelligenta system (IS-lab).
    Learning an Adaptive Dissimilarity Measure for Nearest Neighbour Classification2003Inngår i: Neural computing & applications (Print), ISSN 0941-0643, E-ISSN 1433-3058, Vol. 11, nr 3-4, s. 203-209Artikkel i tidsskrift (Fagfellevurdert)
    Abstract [en]

    In this paper, an approach to weighting features for classification based on the nearest-neighbour rules is proposed. The weights are adaptive in the sense that the weight values are different in various regions of the feature space. The values of the weights are found by performing a random search in the weight space. A correct classification rate is the criterion maximised during the search. Experimentally, we have shown that the proposed approach is useful for classification. The weight values obtained during the experiments show that the importance of features may be different in different regions of the feature space

  • 46526.
    Verikas, Antanas
    et al.
    Högskolan i Halmstad, Sektionen för Informationsvetenskap, Data– och Elektroteknik (IDE), Halmstad Embedded and Intelligent Systems Research (EIS).
    Bacauskiene, Marija
    Department of Applied Electronics, Kaunas University of Technology, LT-3031, Kaunas, Lithuania.
    Feature Selection with Neural Networks2002Inngår i: Pattern Recognition Letters, ISSN 0167-8655, E-ISSN 1872-7344, Vol. 23, nr 11, s. 1323-1335Artikkel i tidsskrift (Fagfellevurdert)
    Abstract [en]

    We present a neural network based approach for identifying salient features for classification in feedforward neural networks. Our approach involves neural network training with an augmented cross-entropy error function. The augmented error function forces the neural network to keep low derivatives of the transfer functions of neurons when learning a classification task. Such an approach reduces output sensitivity to the input changes. Feature selection is based on the reaction of the cross-validation data set classification error due to the removal of the individual features. We demonstrate the usefulness of the proposed approach on one artificial and three real-world classification problems. We compared the approach with five other feature selection methods, each of which banks on a different concept. The algorithm developed outperformed the other methods by achieving higher classification accuracy on all the problems tested.

  • 46527.
    Verikas, Antanas
    et al.
    Högskolan i Halmstad, Sektionen för Informationsvetenskap, Data– och Elektroteknik (IDE), Halmstad Embedded and Intelligent Systems Research (EIS).
    Bacauskiene, Marija
    Kaunas University of Technology, Lithuania.
    Gelzinis, Adas
    Kaunas University of Technology, Lithuania.
    Leverages Based Neural Networks Fusion2004Inngår i: Neural information processing, 2004, s. 446-451Konferansepaper (Fagfellevurdert)
    Abstract [en]

    To improve estimation results, outputs of multiple neural networks can be aggregated into a committee output. In this paper, we study the usefulness of the leverages based information for creating accurate neural network committees. Based on the approximate leave-one-out error and the suggested, generalization error based, diversity test, accurate and diverse networks are selected and fused into a committee using data dependent aggregation weights. Four data dependent aggregation schemes – based on local variance, covariance, Choquet integral, and the generalized Choquet integral – are investigated. The effectiveness of the approaches is tested on one artificial and three real world data sets.

  • 46528.
    Verikas, Antanas
    et al.
    Högskolan i Halmstad, Akademin för informationsteknologi, Halmstad Embedded and Intelligent Systems Research (EIS), CAISR Centrum för tillämpade intelligenta system (IS-lab).
    Bacauskiene, Marija
    Department of Applied Electronics, Kaunas University of Technology, Studentu 50, LT-3031, Kaunas, Lithuania.
    Malmqvist, Kerstin
    Högskolan i Halmstad, Akademin för informationsteknologi, Halmstad Embedded and Intelligent Systems Research (EIS).
    Selecting salient features for classification committees2003Inngår i: Artificial Neural Networks and Neural Information Processing — ICANN/ICONIP 2003 / [ed] Kaynak, O Alpaydin, E Oja, E Xu, L, Heidelberg: Springer Berlin/Heidelberg, 2003, Vol. 2714, s. 35-42Konferansepaper (Fagfellevurdert)
    Abstract [en]

    We present a neural network based approach for identifying salient features for classification in neural network committees. Our approach involves neural network training with an augmented cross-entropy error function. The augmented error function forces the neural network to keep low derivatives of the transfer functions of neurons of the network when learning a classification task. Feature selection is based on two criteria, namely the reaction of the cross-validation data set classification error due to the removal of the individual features and the diversity of neural networks comprising the committee. The algorithm developed removed a large number of features from the original data sets without reducing the classification accuracy of the committees. By contrast, the accuracy of the committees utilizing the reduced feature sets was higher than those exploiting all the original features. © Springer-Verlag Berlin Heidelberg 2003.

  • 46529.
    Verikas, Antanas
    et al.
    Högskolan i Halmstad, Sektionen för Informationsvetenskap, Data– och Elektroteknik (IDE), Halmstad Embedded and Intelligent Systems Research (EIS).
    Gelzinis, Adas
    Department of Applied Electronics, Kaunas University of Technology, Lithuania.
    Training neural networks by stochastic optimisation2000Inngår i: Neurocomputing, ISSN 0925-2312, E-ISSN 1872-8286, Vol. 30, nr 1-4, s. 153-172Artikkel i tidsskrift (Fagfellevurdert)
    Abstract [en]

    We present a stochastic learning algorithm for neural networks. The algorithm does not make any assumptions about transfer functions of individual neurons and does not depend on a functional form of a performance measure. The algorithm uses a random step of varying size to adapt weights. The average size of the step decreases during learning. The large steps enable the algorithm to jump over local maxima/minima, while the small ones ensure convergence in a local area. We investigate convergence properties of the proposed algorithm as well as test the algorithm on four supervised and unsupervised learning problems. We have found a superiority of this algorithm compared to several known algorithms when testing them on generated as well as real data.

  • 46530.
    Verikas, Antanas
    et al.
    Högskolan i Halmstad, Sektionen för Informationsvetenskap, Data– och Elektroteknik (IDE), Halmstad Embedded and Intelligent Systems Research (EIS), Intelligenta system (IS-lab).
    Gelzinis, Adas
    Department of Electrical & Control Equipment, Kaunas University of Technology, Kaunas, Lithuania.
    Bacauskiene, Marija
    Department of Electrical & Control Equipment, Kaunas University of Technology, Kaunas, Lithuania.
    Mining data with random forests: A survey and results of new tests2011Inngår i: Pattern Recognition, ISSN 0031-3203, E-ISSN 1873-5142, Vol. 44, nr 2, s. 330-349Artikkel i tidsskrift (Fagfellevurdert)
    Abstract [en]

    Random forests (RF) has become a popular technique for classification, prediction, studying variable importance, variable selection, and outlier detection. There are numerous application examples of RF in a variety of fields. Several large scale comparisons including RF have been performed. There are numerous articles, where variable importance evaluations based on the variable importance measures available from RF are used for data exploration and understanding. Apart from the literature survey in RF area, this paper also presents results of new tests regarding variable rankings based on RF variable importance measures. We studied experimentally the consistency and generality of such rankings. Results of the studies indicate that there is no evidence supporting the belief in generality of such rankings. A high variance of variable importance evaluations was observed in the case of small number of trees and small data sets.

  • 46531.
    Verikas, Antanas
    et al.
    Högskolan i Halmstad, Akademin för informationsteknologi, Halmstad Embedded and Intelligent Systems Research (EIS). Kaunas University of Technology, Kaunas, Lithuania.
    Gelzinis, Adas
    Kaunas University of Technology, Kaunas, Lithuania.
    Bacauskiene, Marija
    Kaunas University of Technology, Kaunas, Lithuania.
    Hållander, Magnus
    Högskolan i Halmstad, Akademin för informationsteknologi, Halmstad Embedded and Intelligent Systems Research (EIS), Intelligenta system (IS-lab).
    Uloza, Virgilijus
    Kaunas University of Medicine, Kaunas, Lithuania.
    Kaseta, Marius
    Kaunas University of Medicine, Kaunas, Lithuania.
    Combining image, voice, and the patient's questionnaire data to categorize laryngeal disorders2010Inngår i: Artificial Intelligence in Medicine, ISSN 0933-3657, E-ISSN 1873-2860, Vol. 49, nr 1, s. 43-50Artikkel i tidsskrift (Fagfellevurdert)
    Abstract [en]

    Objective: This paper is concerned with soft computing techniques for categorizing laryngeal disorders based on information extracted from an image of patient's vocal folds, a voice signal, and questionnaire data.

    Methods: Multiple feature sets are exploited to characterize images and voice signals. To characterize colour, texture, and geometry of biological structures seen in colour images of vocal folds, eight feature sets are used. Twelve feature sets are used to obtain a comprehensive characterization of a voice signal (the sustained phonation of the vowel sound /a/). Answers to 14 questions constitute the questionnaire feature set. A committee of support vector machines is designed for categorizing the image, voice, and query data represented by the multiple feature sets into the healthy, nodular and diffuse classes. Five alternatives to aggregate separate SVMs into a committee are explored. Feature selection and classifier design are combined into the same learning process based on genetic search.

    Results: Data of all the three modalities were available from 240 patients. Among those, 151 patients belong to the nodular class, 64 to the diffuse class and 25 to the healthy class. When using a single feature set to characterize each modality, the test set data classification accuracy of 75.0%, 72.1%, and 85.0% was obtained for the image, voice and questionnaire data, respectively. The use of multiple feature sets allowed to increase the accuracy to 89.5% and 87.7% for the image and voice data, respectively. The test set data classification accuracy of over 98.0% was obtained from a committee exploiting multiple feature sets from all the three modalities. The highest classification accuracy was achieved when using the SVM-based aggregation with hyper parameters of the SVM determined by genetic search. Bearing in mind the difficulty of the task, the obtained classification accuracy is rather encouraging.

    Conclusions: Combination of both multiple feature sets characterizing a single modality and the three modalities allowed to substantially improve the classification accuracy if compared to the highest accuracy obtained from a single feature set and a single modality. In spite of the unbalanced data sets used, the error rates obtained for the three classes were rather similar.

  • 46532.
    Verikas, Antanas
    et al.
    Högskolan i Halmstad, Akademin för informationsteknologi, Halmstad Embedded and Intelligent Systems Research (EIS), Intelligenta system (IS-lab).
    Gelzinis, Adas
    Kaunas University of Technology, Kaunas, Lithuania .
    Bacauskiene, Marija
    Kaunas University of Technology, Kaunas, Lithuania .
    Olenina, Irina
    Klaipeda University, Kaunas, Lithuania.
    Olenin, Sergej
    Klaipeda University, Klaipeda, Lithuania.
    Vaiciukynas, Evaldas
    Kaunas University of Technology, Kaunas, Lithuania .
    Automated image analysis- and soft computing-based detection of the invasive dinoflagellate Prorocentrum minimum (Pavillard) Schiller2012Inngår i: Expert systems with applications, ISSN 0957-4174, E-ISSN 1873-6793, Vol. 39, nr 5, s. 6069-6077Artikkel i tidsskrift (Fagfellevurdert)
    Abstract [en]

    A long term goal of this work is an automated system for image analysis- and soft computing-based detection, recognition, and derivation of quantitative concentration estimates of different phytoplankton species using a simple imaging system. This article is limited, however, to detection of objects in phytoplankton images, especially objects representing one invasive species-Prorocentrum minimum (P. minimum), which is known to cause harmful blooms in many estuarine and coastal environments. A new technique, combining phase congruency-based detection of circular objects, stochastic optimization, and image segmentation was developed for solving the task. The developed algorithms were tested using 114 images of 1280 × 960 pixels size recorded by a colour camera. There were 2088 objects representing P. minimum cells in the images in total. The algorithms were able to detect 93.25% of the objects. Bearing in mind simplicity of the imaging system used the result is rather encouraging and may be applied for future development of the algorithms aimed at automated classification of objects into classes representing different phytoplankton species. © 2011 Elsevier Ltd. All rights reserved.

  • 46533.
    Verikas, Antanas
    et al.
    Högskolan i Halmstad, Akademin för informationsteknologi, Halmstad Embedded and Intelligent Systems Research (EIS), Intelligenta system (IS-lab). Kaunas University of Technology, Kaunas, Lithuania.
    Gelzinis, Adas
    Kaunas University of Technology, Kaunas, Lithuania.
    Bacauskiene, Marija
    Kaunas University of Technology, Kaunas, Lithuania .
    Olenina, Irina
    Klaipeda University, Klaipeda, Lithuania.
    Olenin, Sergej
    Klaipeda University, Klaipeda, Lithuania .
    Vaiciukynas, Evaldas
    Kaunas University of Technology, Kaunas, Lithuania.
    Phase congruency-based detection of circular objects applied to analysis of phytoplankton images2012Inngår i: Pattern Recognition, ISSN 0031-3203, E-ISSN 1873-5142, Vol. 45, nr 4, s. 1659-1670Artikkel i tidsskrift (Fagfellevurdert)
    Abstract [en]

    Detection and recognition of objects representing the Prorocentrum minimum (P. minimum) species in phytoplankton images is the main objective of the article. The species is known to cause harmful blooms in many estuarine and coastal environments. A new technique, combining phase congruency-based detection of circular objects in images, stochastic optimization-based object contour determination, and SVM- as well as random forest (RF)-based classification of objects was developed to solve the task. A set of various features including a subset of new features computed from phase congruency preprocessed images was used to characterize extracted objects. The developed algorithms were tested using 114 images of 1280×960 pixels. There were 2088 P. minimum cells in the images in total. The algorithms were able to detect 93.25% of objects representing P. minimum cells and correctly classified 94.9% of all detected objects. The feature set used has shown considerable tolerance to out-of-focus distortions. The obtained results are rather encouraging and will be used to develop an automated system for obtaining abundance estimates of the species. © 2011 Elsevier Ltd All rights reserved.

  • 46534.
    Verikas, Antanas
    et al.
    Högskolan i Halmstad, Sektionen för Informationsvetenskap, Data– och Elektroteknik (IDE), Halmstad Embedded and Intelligent Systems Research (EIS).
    Gelzinis, Adas
    Department of Applied Electronics, Kaunas University of Technology, Lithuania.
    Bacauskiene, Marija
    Department of Applied Electronics, Kaunas University of Technology, Lithuania.
    Uloza, Virgilijus
    Department of Otolaryngology, Kaunas University of Medicine, Lithuania.
    Integrating global and local analysis of color, texture and geometrical information for categorizing laryngeal images2006Inngår i: International journal of pattern recognition and artificial intelligence, ISSN 0218-0014, Vol. 20, nr 8, s. 1187-1205Artikkel i tidsskrift (Fagfellevurdert)
    Abstract [en]

    An approach to integrating the global and local kernel-based automated analysis of vocal fold images aiming to categorize laryngeal diseases is presented in this paper. The problem is treated as an image analysis and recognition task. A committee of support vector machines is employed for performing the categorization of vocal fold images into healthy, diffuse and nodular classes. Analysis of image color distribution, Gabor filtering, cooccurrence matrices, analysis of color edges, image segmentation into homogeneous regions from the image color, texture and geometry view point, analysis of the soft membership of the regions in the decision classes, the kernel principal components based feature extraction are the techniques employed for the global and local analysis of laryngeal images. Bearing in mind the high similarity of the decision classes, the correct classification rate of over 94% obtained when testing the system on 785 vocal fold images is rather encouraging.

  • 46535.
    Verikas, Antanas
    et al.
    Högskolan i Halmstad, Sektionen för Informationsvetenskap, Data– och Elektroteknik (IDE), Halmstad Embedded and Intelligent Systems Research (EIS).
    Gelzinis, Adas
    Kaunas University of Technology, Lithuania.
    Bacauskiene, Marija
    Kaunas University of Technology, Lithuania.
    Uloza, Virgilijus
    Kaunas University of Medicine, Kaunas, Lithuania.
    Intelligent vocal cord image analysis for categorizing laryngeal diseases2005Inngår i: Innovations in applied artificial intelligence / [ed] Moonis Ali, Floriana Esposito, Springer, 2005, s. 69-78Konferansepaper (Fagfellevurdert)
    Abstract [en]

    Colour, shape, geometry, contrast, irregularity and roughness of the visual appearance of vocal cords are the main visual features used by a physician to diagnose laryngeal diseases. This type of examination is rather subjective and to a great extent depends on physician’s experience. A decision support system for automated analysis of vocal cord images, created exploiting numerous vocal cord images can be a valuable tool enabling increased reliability of the analysis, and decreased intra- and inter-observer variability. This paper is concerned with such a system for analysis of vocal cord images. Colour, texture, and geometrical features are used to extract relevant information. A committee of artificial neural networks is then employed for performing the categorization of vocal cord images into healthy, diffuse, and nodular classes. A correct classification rate of over 93% was obtained when testing the system on 785 vocal cord images.

  • 46536.
    Verikas, Antanas
    et al.
    Högskolan i Halmstad, Sektionen för Informationsvetenskap, Data– och Elektroteknik (IDE), Halmstad Embedded and Intelligent Systems Research (EIS).
    Gelzinis, Adas
    Kaunas University of Technology.
    Bacauskiene, Marija
    Kaunas University of Technology.
    Uloza, Virgilijus
    Kaunas University of Medicine.
    Towards noninvasive screening for malignant tumours in human larynx2010Inngår i: Medical Engineering and Physics, ISSN 1350-4533, E-ISSN 1873-4030, Vol. 32, nr 1, s. 83-89Artikkel i tidsskrift (Fagfellevurdert)
    Abstract [en]

    This article is concerned with soft computing-based noninvasive screening for malignant disorders in human larynx. The suitability of two types of data for the analysis is explored. The questionnaire data and the digital voice recordings of the sustained phonation of the vowel sound /a/ are the data types considered in this study. The screening is considered as a task of data classification into the healthy, cancerous, and noncancerous classes. To explore data and decisions a nonlinear mapping technique exhibiting the property of local data ordering is applied. The classification accuracy of over 92% was obtained for unseen questionnaire data collected from 240 subjects. The experimental investigations have shown that, concerning the three classes, the questionnaire data carry much more discriminative information than the voice signal. Two-dimensional plots created using the mapping technique provide further insights into the data and decisions obtained from the classifiers.

  • 46537.
    Verikas, Antanas
    et al.
    Högskolan i Halmstad, Sektionen för Informationsvetenskap, Data– och Elektroteknik (IDE), Halmstad Embedded and Intelligent Systems Research (EIS), Intelligenta system (IS-lab).
    Gelzinis, Adas
    Kaunas University of Technology, Department of Electrical and Control Equipment, Kaunas, Lithuania .
    Hållander, Magnus
    Högskolan i Halmstad, Sektionen för Informationsvetenskap, Data– och Elektroteknik (IDE), Halmstad Embedded and Intelligent Systems Research (EIS).
    Bacauskiene, Marija
    Kaunas University of Technology, Department of Electrical and Control Equipment, Kaunas, Lithuania .
    Alzghoul, Ahmad
    Högskolan i Halmstad, Sektionen för Informationsvetenskap, Data– och Elektroteknik (IDE), Halmstad Embedded and Intelligent Systems Research (EIS).
    Screening web breaks in a pressroom by soft computing2011Inngår i: Applied Soft Computing, ISSN 1568-4946, E-ISSN 1872-9681, Vol. 11, nr 3, s. 3114-3124Artikkel i tidsskrift (Fagfellevurdert)
    Abstract [en]

    The objective of this work is to identify the main parameters of the printing press, the printing process, and the paper affecting the occurrence of web breaks in a pressroom. Two approaches are explored. The first one treats the problem as a task of data classification into "break" and "non-break" classes. The procedures of classifier design and selection of relevant input variables are integrated into one process based on genetic search. The second approach, targeted for data visualization and also based on genetic search, combines procedures of input variable selection and data mapping into a two-dimensional space. The genetic search-based analysis has shown that the web tension parameters are amongst the most important ones. It was also found that the group of paper related parameters recorded online contain more information for predicting the occurrence of web breaks than the group of traditional parameters recorded off-line at a paper lab. Using the selected set of parameters, on average, 93.7% of the test set data were classified correctly. The average classification accuracy of web break cases was equal to 76.7%. (C) 2010 Elsevier B. V. All rights reserved.

  • 46538.
    Verikas, Antanas
    et al.
    Högskolan i Halmstad, Sektionen för Informationsvetenskap, Data– och Elektroteknik (IDE), Halmstad Embedded and Intelligent Systems Research (EIS).
    Gelzinis, Adas
    Kaunas University of Technology.
    Kovalenko, Marina
    Kaunas University of Technology.
    Bacauskiene, Marija
    Kaunas University of Technology.
    Selecting features from multiple feature sets for SVM committee-based screening of human larynx2010Inngår i: Expert systems with applications, ISSN 0957-4174, E-ISSN 1873-6793, Vol. 37, nr 10, s. 6957-6962Artikkel i tidsskrift (Fagfellevurdert)
    Abstract [en]

    This paper is concerned with a two stage procedure for designing a sequential SVM committee and selecting features for the committee from multiple feature sets. It is assumed that features of one type comprise one feature set. Selection of both features and hyper-parameters of SVM classifiers comprising the committee is integrated into one learning process based on genetic search. The designing process focuses on feature selection for pair-wise classification implemented by the SVM. In the first stage, a series of pair-wise SVM are designed starting from the original feature sets as well as from sets created by simple random selection from the original ones. Outputs of the SVM are then converted into probabilities and used as inputs to the second stage SVM. When testing the technique in a three-class classification problem of voice data, a statistically significant improvement in classification accuracy was obtained if compared to parallel committees. The number of feature types and features selected for the pair-wise classification are class specific.

  • 46539.
    Verikas, Antanas
    et al.
    Högskolan i Halmstad, Sektionen för Informationsvetenskap, Data– och Elektroteknik (IDE), Halmstad Embedded and Intelligent Systems Research (EIS).
    Gelzinis, Adas
    Kaunas University of Technology, Kaunas, Lithuania.
    Malmqvist, Kerstin
    Högskolan i Halmstad, Sektionen för Informationsvetenskap, Data– och Elektroteknik (IDE), Halmstad Embedded and Intelligent Systems Research (EIS).
    Using Labelled and Unlabelled Data to Train a Multilayer Perceptron for Colour Classification in Graphic Arts1999Inngår i: Multiple approaches to intelligent systems: 12th International Conference on Industrial and Engineering Applications of Artificial Intelligence and Expert Systems IEA/AIE-99, Cairo, Egypt, May 31 - June 3, 1999. Proceedings / [ed] Ibrahim Imam, Yves Kodratoff, Ayman El-Dessouki and Moonis Ali, Berlin: Springer Berlin/Heidelberg, 1999, s. 550-559Konferansepaper (Fagfellevurdert)
    Abstract [en]

    This paper presents an approach to using both labelled and unlabelled data to train a multi-layer perceptron. The unlabelled data are iteratively pre-processed by a perceptron being trained to obtain the soft class label estimates. It is demonstrated that substantial gains in classification performance may be achieved from the use of the approach when the labelled data do not adequately represent the entire class distributions. The experimental investigations performed have shown that the approach proposed may be successfully used to train networks for colour classification in graphic arts.

  • 46540.
    Verikas, Antanas
    et al.
    Högskolan i Halmstad, Sektionen för Informationsvetenskap, Data– och Elektroteknik (IDE), Halmstad Embedded and Intelligent Systems Research (EIS).
    Gelzinis, Adas
    Kaunas University of Technology, Lithuania.
    Malmqvist, Kerstin
    Högskolan i Halmstad, Sektionen för Informationsvetenskap, Data– och Elektroteknik (IDE), Halmstad Embedded and Intelligent Systems Research (EIS), Intelligenta system (IS-lab).
    Using unlabelled data to train a multilayer perceptron2001Inngår i: Neural Processing Letters, ISSN 1370-4621, E-ISSN 1573-773X, Vol. 14, nr 3, s. 179-201Artikkel i tidsskrift (Fagfellevurdert)
    Abstract [en]

    This Letter presents an approach to using both labelled and unlabelled data to train a multilayer perceptron. The unlabelled data are iteratively pre-processed by a perceptron being trained to obtain the soft class label estimates. It is demonstrated that substantial gains in classification performance may be achieved from the use of the approach when the labelled data do not adequately represent the entire class distributions. The experimental investigations performed have shown that the approach proposed may be successfully used to train neural networks for learning different classification problems.

  • 46541.
    Verikas, Antanas
    et al.
    Högskolan i Halmstad, Sektionen för Informationsvetenskap, Data– och Elektroteknik (IDE), Halmstad Embedded and Intelligent Systems Research (EIS), Laboratoriet för intelligenta system.
    Guzaitis, Jonas
    Kaunas University of Technology.
    Gelzinis, Adas
    Kaunas University of Technology.
    Bacauskiene, Marija
    Kaunas University of Technology.
    A general framework for designing a fuzzy rule-based classifier2011Inngår i: Knowledge and Information Systems, ISSN 0219-1377, E-ISSN 0219-3116, Vol. 29, nr 1, s. 203-221Artikkel i tidsskrift (Fagfellevurdert)
    Abstract [en]

    This paper presents a general framework for designing a fuzzyrule-based classifier. Structure and parameters of the classifierare evolved through a two-stage genetic search. To reduce the searchspace, the classifier structure is constrained by a tree createdusing the evolving SOM tree algorithm. Salient input variables arespecific for each fuzzy rule and are found during the genetic searchprocess. It is shown through computer simulations of four real worldproblems that a large number of rules and input variables can beeliminated from the model without deteriorating the classificationaccuracy. By contrast, the classification accuracy of unseen data isincreased due to the elimination.This paper presents a general framework for designing a fuzzyrule-based classifier. Structure and parameters of the classifierare evolved through a two-stage genetic search. To reduce the searchspace, the classifier structure is constrained by a tree createdusing the evolving SOM tree algorithm. Salient input variables arespecific for each fuzzy rule and are found during the genetic searchprocess. It is shown through computer simulations of four real worldproblems that a large number of rules and input variables can beeliminated from the model without deteriorating the classificationaccuracy. By contrast, the classification accuracy of unseen data isincreased due to the elimination.

  • 46542.
    Verikas, Antanas
    et al.
    Högskolan i Halmstad, Akademin för informationsteknologi, Halmstad Embedded and Intelligent Systems Research (EIS), Intelligenta system (IS-lab).
    Kalsyte, Zivile
    Department of Electrical and Control Instrumentation, Kaunas University of Technology, Kaunas, Lithuania.
    Bacauskiene, Marija
    Department of Electrical and Control Instrumentation, Kaunas University of Technology, Kaunas, Lithuania.
    Gelzinis, Adas
    Department of Electrical and Control Instrumentation, Kaunas University of Technology, Kaunas, Lithuania.
    Hybrid and ensemble-based soft computing techniques in bankruptcy prediction: a survey2010Inngår i: Soft Computing - A Fusion of Foundations, Methodologies and Applications, ISSN 1432-7643, E-ISSN 1433-7479, Vol. 14, nr 9, s. 995-1010Artikkel i tidsskrift (Fagfellevurdert)
    Abstract [en]

    This paper presents a comprehensive review of hybrid and ensemble-based soft computing techniques applied to bankruptcy prediction. A variety of soft computing techniques are being applied to bankruptcy prediction. Our focus is on techniques, namely how different techniques are combined, but not on obtained results. Almost all authors demonstrate that the technique they propose outperforms some other methods chosen for the comparison. However, due to different data sets used by different authors and bearing in mind the fact that confidence intervals for the prediction accuracies are seldom provided, fair comparison of results obtained by different authors is hardly possible. Simulations covering a large variety of techniques and data sets are needed for a fair comparison. We call a technique hybrid if several soft computing approaches are applied in the analysis and only one predictor is used to make the final prediction. In contrast, outputs of several predictors are combined, to obtain an ensemble-based prediction.

  • 46543.
    Verikas, Antanas
    et al.
    Högskolan i Halmstad, Sektionen för Informationsvetenskap, Data– och Elektroteknik (IDE), Halmstad Embedded and Intelligent Systems Research (EIS).
    Lipnickas, Arunas
    Department of Applied Electronics, Kaunas University of Technology, Lithuania.
    Malmqvist, Kerstin
    Högskolan i Halmstad, Sektionen för Informationsvetenskap, Data– och Elektroteknik (IDE), Halmstad Embedded and Intelligent Systems Research (EIS), Intelligenta system (IS-lab).
    Selecting neural networks for a committee decision2002Inngår i: International Journal of Neural Systems, ISSN 0129-0657, E-ISSN 1793-6462, Vol. 12, nr 5, s. 351-361Artikkel i tidsskrift (Fagfellevurdert)
    Abstract [en]

    To improve recognition results, decisions of multiple neural networks can be aggregated into a committee decision. In contrast to the ordinary approach of utilizing all neural networks available to make a committee decision, we propose creating adaptive committees, which are specific for each input data point. A prediction network is used to identify classification neural networks to be fused for making a committee decision about a given input data point. The jth output value of the prediction network expresses the expectation level that the jth classification neural network will make a correct decision about the class label of a given input data point. The proposed technique is tested in three aggregation schemes, namely majority vote, averaging, and aggregation by the median rule and compared with the ordinary neural networks fusion approach. The effectiveness of the approach is demonstrated on two artificial and three real data sets.

  • 46544.
    Verikas, Antanas
    et al.
    Högskolan i Halmstad, Akademin för informationsteknologi, Halmstad Embedded and Intelligent Systems Research (EIS), CAISR Centrum för tillämpade intelligenta system (IS-lab).
    Lipnickas, Arunas
    Kaunas University of Technology, Department of Applied Electronics, Studentu 50, 3031, Kaunas, Lithuania.
    Malmqvist, Kerstin
    Högskolan i Halmstad, Akademin för informationsteknologi, Halmstad Embedded and Intelligent Systems Research (EIS).
    Selecting neural networks for making a committee decision2002Inngår i: ARTIFICIAL NEURAL NETWORKS - ICANN 2002 / [ed] Dorronsoro, J R, Berlin: Springer Berlin/Heidelberg, 2002, Vol. 2415, s. 420-425Konferansepaper (Fagfellevurdert)
    Abstract [en]

    To improve recognition results, decisions of multiple neural networks can be aggregated into a committee decision. In contrast to the ordinary approach of utilizing all neural networks available to make a committee decision, we propose creating adaptive committees, which are specific for each input data point. A prediction network is used to identify classification neural networks to be fused for making a committee decision about a given input data point. The jth output value of the prediction network expresses the expectation level that the jth classification neural network will make a correct decision about the class label of a given input data point. The effectiveness of the approach is demonstrated on two artificial and three real data sets.

  • 46545.
    Verikas, Antanas
    et al.
    Högskolan i Halmstad, Sektionen för Informationsvetenskap, Data– och Elektroteknik (IDE), Halmstad Embedded and Intelligent Systems Research (EIS).
    Lipnickas, Arunas
    Department of Applied Electronics, Kaunas University of Technology, Lithuania.
    Malmqvist, Kerstin
    Högskolan i Halmstad, Sektionen för Informationsvetenskap, Data– och Elektroteknik (IDE), Halmstad Embedded and Intelligent Systems Research (EIS), Intelligenta system (IS-lab).
    Bacauskiene, Marija
    Department of Applied Electronics, Kaunas University of Technology, Lithuania.
    Gelzinis, Adas
    Department of Applied Electronics, Kaunas University of Technology, Lithuania.
    Soft combination of neural classifiers: a comparative study1999Inngår i: Pattern Recognition Letters, ISSN 0167-8655, E-ISSN 1872-7344, Vol. 20, nr 4, s. 429-444Artikkel i tidsskrift (Fagfellevurdert)
    Abstract [en]

    This paper presents four schemes for soft fusion of the outputs of multiple classifiers. In the first three approaches, the weights assigned to the classifiers or groups of them are data dependent. The first approach involves the calculation of fuzzy integrals. The second scheme performs weighted averaging with data-dependent weights. The third approach performs linear combination of the outputs of classifiers via the BADD defuzzification strategy. In the last scheme, the outputs of multiple classifiers are combined using Zimmermann's compensatory operator. An empirical evaluation using widely accessible data sets substantiates the validity of the approaches with data-dependent weights, compared to various existing combination schemes of multiple classifiers.

  • 46546.
    Verikas, Antanas
    et al.
    Högskolan i Halmstad, Sektionen för Informationsvetenskap, Data– och Elektroteknik (IDE), Halmstad Embedded and Intelligent Systems Research (EIS), Intelligenta system (IS-lab).
    Lundström, Jens
    Högskolan i Halmstad, Sektionen för Informationsvetenskap, Data– och Elektroteknik (IDE), Halmstad Embedded and Intelligent Systems Research (EIS), Intelligenta system (IS-lab).
    Bacauskiene, Marija
    Department of Electrical and Control Equipment, Kaunas University of Technology, Studentu 50, LT-51368 Kaunas, Lithuania.
    Gelzinis, Adas
    Department of Electrical and Control Equipment, Kaunas University of Technology, Studentu 50, LT-51368 Kaunas, Lithuania.
    Advances in computational intelligence-based print quality assessment and control in offset colour printing2011Inngår i: Expert systems with applications, ISSN 0957-4174, E-ISSN 1873-6793, Vol. 38, nr 10, s. 13441-13447Artikkel i tidsskrift (Fagfellevurdert)
    Abstract [en]

    Nowadays most of information processing steps in printing industry are highly automated, except the last one – print quality assessment and control. Usually quality assessment is a manual, tedious, and subjective procedure. This article presents a survey of non numerous developments in the field of computational intelligence-based print quality assessment and control in offset colour printing. Recent achievements in this area and advances in applied computational intelligence, expert and decision support systems lay good foundations for creating practical tools to automate the last step of the printing process.

  • 46547.
    Verikas, Antanas
    et al.
    Högskolan i Halmstad, Sektionen för Informationsvetenskap, Data– och Elektroteknik (IDE), Halmstad Embedded and Intelligent Systems Research (EIS).
    Malmqvist, Kerstin
    Högskolan i Halmstad, Sektionen för Informationsvetenskap, Data– och Elektroteknik (IDE), Halmstad Embedded and Intelligent Systems Research (EIS), Intelligenta system (IS-lab).
    Bacauskiene, Marija
    Department of Applied Electronics, Kaunas University of Technology, Lithuania.
    Combining neural networks, fuzzy sets, and the evidence theory based techniques for detecting colour specks2001Inngår i: Journal of Intelligent & Fuzzy Systems, ISSN 1064-1246, E-ISSN 1875-8967, Vol. 10, nr 2, s. 117-130Artikkel i tidsskrift (Fagfellevurdert)
    Abstract [en]

    An approach to detecting colour specks in an image taken from a pulp sample of recycled paper is presented. The task is solved through pixel-wise colour classification by an artificial neural network and post-processing based on the evidence theory. The network is trained using possibilistic target values, which are determined through a self-organising process in a 2D and 1D map of chromaticity and lightness, respectively. The problem of post-processing of a pixelwise-classified image is addressed from the point of view of the Dempster-Shafer theory of evidence. Each neighbour of a pixel being analysed is considered as an item of evidence supporting particular hypotheses regarding the class label of that pixel. The strength of support is defined as a function of the degree of uncertainty in class label of the neighbour, and the distance between the neighbour and the pixel being considered. The experiments performed have shown that the colour classification results correspond well with the human perception of colours of the specks.

  • 46548.
    Verikas, Antanas
    et al.
    Högskolan i Halmstad, Akademin för informationsteknologi, Halmstad Embedded and Intelligent Systems Research (EIS).
    Malmqvist, Kerstin
    Högskolan i Halmstad, Akademin för informationsteknologi, Halmstad Embedded and Intelligent Systems Research (EIS), Intelligenta system (IS-lab).
    Bergman, L.
    Högskolan i Halmstad, Akademin för informationsteknologi, Halmstad Embedded and Intelligent Systems Research (EIS), Intelligenta system (IS-lab).
    Neural networks based colour measuring for process monitoring and control in multicoloured newspaper printing2000Inngår i: Neural computing & applications (Print), ISSN 0941-0643, E-ISSN 1433-3058, Vol. 9, nr 3, s. 227-242Artikkel i tidsskrift (Fagfellevurdert)
    Abstract [en]

    This paper presents a neural networks based method and a system for colour measurements on printed halftone multicoloured pictures and halftone multicoloured bars in newspapers. The measured values, called a colour vector, are used by the operator controlling the printing process to make appropriate ink feed adjustments to compensate for colour deviations of the picture being measured from the desired print. By the colour vector concept, we mean the CMY or CMYK (cyan, magenta, yellow and black) vector, which lives in the three- or four-dimensional space of printing inks. Two factors contribute to values of the vector components, namely the percentage of the area covered by cyan, magenta, yellow and black inks (tonal values) and ink densities. Values of the colour vector components increase if tonal values or ink densities rise, and vice versa. If some reference values of the colour vector components are set from a desired print, then after an appropriate calibration, the colour vector measured on an actual halftone multicoloured area directly shows how much the operator needs to raise or lower the cyan, magenta, yellow and black ink densities to compensate for colour deviation from the desired print. The 18 months experience of the use of the system in the printing shop witnesses its usefulness through the improved quality of multicoloured pictures, the reduced consumption of inks and, therefore, less severe problems of smearing and printing through.

  • 46549.
    Verikas, Antanas
    et al.
    Högskolan i Halmstad, Sektionen för Informationsvetenskap, Data– och Elektroteknik (IDE), Halmstad Embedded and Intelligent Systems Research (EIS), Intelligenta system (IS-lab).
    Uloza, Virgilijus
    Department of Otolaryngology, Kaunas University of Medicine, Kaunas 50009, Lithuania.
    Bacauskiene, Marija
    Department of Applied Electronics, Kaunas University of Technology, Studentu 50, Kaunas 51368, Lithuania.
    Gelzinis, Adas
    Department of Applied Electronics, Kaunas University of Technology, Studentu 50, Kaunas 51368, Lithuania.
    Kelertas, Edgaras
    Department of Applied Electronics, Kaunas University of Technology, Studentu 50, Kaunas 51368, Lithuania.
    Advances in laryngeal imaging2009Inngår i: European Archives of Oto-Rhino-Laryngology, ISSN 0937-4477, E-ISSN 1434-4726, Vol. 266, nr 10, s. 1509-1520Artikkel i tidsskrift (Fagfellevurdert)
    Abstract [en]

    Imaging and image analysis became an important issue in laryngeal diagnostics. Various techniques, such as videostroboscopy, videokymography, digital kymograpgy, or ultrasonography are available and are used in research and clinical practice. This paper reviews recent advances in imaging for laryngeal diagnostics.

  • 46550.
    Verikas, Antanas
    et al.
    Högskolan i Halmstad, Akademin för informationsteknologi, Halmstad Embedded and Intelligent Systems Research (EIS).
    Uloza, Virgilijus
    Kaunas University of Medicine, Kaunas, Lithuania.
    Gelzinis, Adas
    Kaunas University of Technology, Kaunas, Lithuania.
    Bacauskiene, Marija
    Kaunas University of Technology, Kaunas, Lithuania.
    Pribuisiene, Ruta
    Kaunas University of Medicine, Kaunas, Lithuania.
    Kaseta, Marius
    Kaunas University of Medicine, Kaunas, Lithuania.
    Exploiting image, voice, and patient's questionnaire for screening laryngeal disorders2009Inngår i: Proceedings of the 3rd Advanced Voice Function Assessement International Workshop (AVFA 2009), Madrid, 2009, s. 85-88Konferansepaper (Fagfellevurdert)
    Abstract [en]

    This paper is concerned with soft computing techniques for categorizing laryngeal disorders based on information extracted from an image of patient's vocal folds, a voice signal, and questionnaire data. Multiple feature sets are used to characterize images and voice signals. A committee of support vector machines (SVM) is designed for categorizing the data represented by the multiple feature sets into the healthy, nodular and diffuse classes. The feature selection and classifier design is combined into the same learning process based on genetic search. When testing the developed tools on the set of data collected from 240 patients, the classification accuracy of over 98.0% was obtained. Combination of the three modalities allowed to substantially improve the classification accuracy if compared to the highest accuracy obtained from a single modality.

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