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Cross Site Product Page Classification with Supervised Machine Learning
KTH, School of Computer Science and Communication (CSC).
2016 (English)Independent thesis Advanced level (degree of Master (Two Years)), 20 credits / 30 HE creditsStudent thesisAlternative title
Webbsideöverskridande klassificering av produktsidor med övervakad maskininlärning (Swedish)
Abstract [en]

This work outlines a possible technique for identifying webpages that contain product  specifications. Using support vector machines a product web page classifier was constructed and tested with various settings. The final result for this classifier ended up being 0.958 in precision and 0.796 in recall for product pages. The scores imply that the method could be considered a valid technique in real world web classification tasks if additional features and more data were made available.

Place, publisher, year, edition, pages
2016. , 46 p.
Keyword [en]
svm support vector machine product page classification
National Category
Computer and Information Science
URN: urn:nbn:se:kth:diva-189555OAI: diva2:946837
Subject / course
Computer Technology, Program- and System Development
Educational program
Master of Science in Engineering - Computer Science and Technology
Available from: 2016-07-06 Created: 2016-07-06 Last updated: 2016-07-06Bibliographically approved

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