Please use this identifier to cite or link to this item:
Title: A kernel-based feature weighting for text classification
Authors: Wittek, P.
Tan, C.L. 
Issue Date: 2009
Citation: Wittek, 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.
Abstract: Text 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.
Source Title: Proceedings of the International Joint Conference on Neural Networks
ISBN: 9781424435531
DOI: 10.1109/IJCNN.2009.5179022
Appears in Collections:Staff Publications

Show full item record
Files in This Item:
There are no files associated with this item.


checked on Jun 16, 2019

Page view(s)

checked on Jun 14, 2019

Google ScholarTM



Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.