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Please use this identifier to cite or link to this item: https://doi.org/10.1109/IJCNN.2009.5179022
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dc.titleA kernel-based feature weighting for text classification
dc.contributor.authorWittek, P.
dc.contributor.authorTan, C.L.
dc.date.accessioned2013-07-04T08:36:32Z
dc.date.available2013-07-04T08:36:32Z
dc.date.issued2009
dc.identifier.citationWittek, P.,Tan, C.L. (2009). A kernel-based feature weighting for text classification. Proceedings of the International Joint Conference on Neural Networks : 3373-3379. ScholarBank@NUS Repository. <a href="https://doi.org/10.1109/IJCNN.2009.5179022" target="_blank">https://doi.org/10.1109/IJCNN.2009.5179022</a>
dc.identifier.isbn9781424435531
dc.identifier.urihttp://scholarbank.nus.edu.sg/handle/10635/41817
dc.description.abstractText classification by support vector machines can benefit from semantic smoothing kernels that regard semantic relations among index terms while computing similarity. Adding expansion terms to the vector representation can also improve effectiveness. However, existing semantic smoothing kernels do not employ term expansion. This paper proposes a new nonlinear kernel for text classification to exploit semantic relations between terms to add weighted expansion terms. © 2009 IEEE.
dc.description.urihttp://libproxy1.nus.edu.sg/login?url=http://dx.doi.org/10.1109/IJCNN.2009.5179022
dc.sourceScopus
dc.typeConference Paper
dc.contributor.departmentCOMPUTER SCIENCE
dc.description.doi10.1109/IJCNN.2009.5179022
dc.description.sourcetitleProceedings of the International Joint Conference on Neural Networks
dc.description.page3373-3379
dc.description.coden85OFA
dc.identifier.isiutNOT_IN_WOS
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