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Download fileClass information adapted kernel for support vector machine
conference contribution
posted on 2017-12-06, 00:00 authored by Tasadduq ImamTasadduq Imam, Kevin TickleKevin TickleThis article presents a support vector machine (SVM) learning approach that adapts class information within the kernel computation. Experiments on fifteen publicly available datasets are conducted and the impact of proposed approach for varied settings are observed. It is noted that the new approach generally improves minority class prediction, depicting it as a well-suited scheme for imbalanced data. However, a SVM based customization is also developed that significantly improves prediction performance in terms of different measures. Overall, the proposed method holds promise with potential for future extensions.
History
Parent Title
ICONIP 2010 : Neural information processing : theory and algorithms, 17th international conference, proceedings, part II, 20-25 November 2010, Sydney, AustraliaStart Page
116End Page
123Number of Pages
8Start Date
2010-01-01Finish Date
2010-01-01eISSN
1611-3349ISSN
0302-9743ISBN-13
9783642175336Location
Sydney, AustraliaPublisher
SpringerPlace of Publication
Heidelberg, GermanyFull Text URL
Peer Reviewed
- Yes
Open Access
- No
Era Eligible
- Yes