Abstract
According to statistical learning theory, we propose a feature selection method using support vector machines (SVMs). By exploiting the power of SVMs, we integrate the two tasks, feature selection and classifier training, into a single consistent framework and make the feature selection process more effective. Our experiments show that our SVM feature selection method can speed up the classification process and improve the generalization performance of the classifier.
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© 2004 Springer-Verlag Berlin Heidelberg
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Fan, ZG., Wang, KA., Lu, BL. (2004). Feature Selection for Fast Image Classification with Support Vector Machines. In: Pal, N.R., Kasabov, N., Mudi, R.K., Pal, S., Parui, S.K. (eds) Neural Information Processing. ICONIP 2004. Lecture Notes in Computer Science, vol 3316. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-30499-9_159
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DOI: https://doi.org/10.1007/978-3-540-30499-9_159
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-23931-4
Online ISBN: 978-3-540-30499-9
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