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Using a Bayesian NeuralNetwork as a Tool for DocumentFiltering Considering User Profiles
KTH, School of Computer Science and Communication (CSC).
2013 (English)Independent thesis Basic level (degree of Bachelor), 20 credits / 30 HE creditsStudent thesis
Abstract [en]

This thesis describes methods and problems when using Bayesian Ar-

tificial Neural Networks for text document classification. It also depict

other methods used in text analysis and automated classification in gen-

eral. The main tasks are to construct a network, investigate the effect of

variations to existing parameters and how to combine dependent input

attributes into complex columns. Correlation measures are used to find

these combinations. The basic idea is to let the classifier built work as

a document filtering system. Results from the testing are described and

explained.

The results are discouraging. All tests indicate that the training set

is too small. Compared to another study done, on the same data, at

Swedish Institute of Computer Science the performance of the classifier

is poor.

Abstract [sv]

Den här rapporten beskriver metoder och problem vid användande av

Bayesianska artificiella neuronnät för dokumentklassificering. Det be-

rör även andra metoder som används inom textanalys och automatisk

klassificering. Den huvudsakliga uppgiften är att undersöka effekten av

parametrar och variation av dessa och hur beroende indata attribut

skall kombineras till att skapa komplexa kolumner. För att hitta dessa

kombinationer används korrelationsmått. Grundtanken är att låta den

skapade klassificeraren fungera som ett dokumentfiltreringssystem. Re-

sultat från tester är beskrivna och förklarade.

Resultaten är nedslående. Alla tester tyder på att träningsmängden är

för liten. Jämfört med en annan studie genomförd, på samma data, vid

Swedish Institute of Computer Science så är klassificerarens prestanda

låg.

Place, publisher, year, edition, pages
2013.
Series
Trita-CSC-E, ISSN 1653-5715 ; 13:102
National Category
Computer Science
Identifiers
URN: urn:nbn:se:kth:diva-137421OAI: oai:DiVA.org:kth-137421DiVA: diva2:678898
Educational program
Master of Science in Engineering - Computer Science and Technology
Supervisors
Examiners
Available from: 2013-12-13 Created: 2013-12-13 Last updated: 2013-12-13Bibliographically approved

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Other links

http://www.nada.kth.se/utbildning/grukth/exjobb/rapportlistor/2013/rapporter13/ericmats_magnus_13016.pdf
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School of Computer Science and Communication (CSC)
Computer Science

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