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Complexity in Statistical Relational Learning: A Study on Learning Bayesian Logic Programs
KTH, School of Computer Science and Communication (CSC).
2015 (English)Independent thesis Advanced level (degree of Master (Two Years)), 20 credits / 30 HE creditsStudent thesisAlternative title
Komplexitet i statistiskt relationslärande (Swedish)
Abstract [en]

Most work that is done within machine learning today uses statistical methods which assume that the data is identically and independently distributed. However, the problem domains that we face in the real world are often much more complicated and present both complex relational/logical parts as well as parts with uncertainty. Statistical relational learning (SRL) is a sub-field of machine learning and A.I. that tries to solve these limitations by combining both relational and statistical learning and has become a big research sector in recent years. This thesis will present SRL further and specifically introduce, test and review one of the implementations, namely Bayesian logic programs.

Abstract [sv]

Idag används inom maskininlärning nästan alltid statistiska metoder som antar att datat för lärande är identiskt och oberoende distribuerat. Men de problemområden som vi står inför i den verkliga världen är ofta mycket mer komplicerade och har både komplexa relationella/logiska delar samt osäkerhet. Statistiskt relationslärande (SRL) är en del av maskininlärning och A.I. som försöker lösa dessa begränsningar genom att kombinera både relationer och statistiskt lärande och har på senare år blivit ett stort forskningsområde. Denna avhandling presenterar SRL mer i detalj och utreder och testar en specifik implementation, Bayesianska logikprogram.

Place, publisher, year, edition, pages
Keyword [en]
machine learning, statistical, herbrand, relational, ilp, kreator, learning, bayesian, logic, program, balios, complexity, blp, srl, foi
Keyword [sv]
maskininlärning, relationslärande, herbrand, ilp, kreator, relationer, logik, komplexitet, balios, bayesiansk, blp, srl, foi
National Category
Computer Science
URN: urn:nbn:se:kth:diva-170160OAI: diva2:827501
Subject / course
Computer Science
Educational program
Master of Science in Engineering -Engineering Physics
Available from: 2015-06-29 Created: 2015-06-28 Last updated: 2015-06-29Bibliographically approved

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