Please use this identifier to cite or link to this item: https://doi.org/10.1109/IJCNN.2009.5179022
Title: A kernel-based feature weighting for text classification
Authors: Wittek, P.
Tan, C.L. 
Issue Date: 2009
Source: 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. https://doi.org/10.1109/IJCNN.2009.5179022
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
URI: http://scholarbank.nus.edu.sg/handle/10635/41817
ISBN: 9781424435531
DOI: 10.1109/IJCNN.2009.5179022
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