Abstract
A maximum class distance support vector machine based on the recursive dimension reduction is proposed. This algorithm referring to the concept of fisher linear discriminate analysis is introduced to make the distance between the classes as long as possible along the direction of the discriminate vector, and at the same time a classification hyper-plane with the largest distance between the two classes is achieved. Thus the classification hyper-plane can effectively consist with the distribution of samples, resulting to higher classification accuracy. This paper presents the recursive dimension reduction algorithm and its details. Finally, a simulation illustrates the effectiveness of the presented algorithm.
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© 2009 Springer-Verlag Berlin Heidelberg
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Sun, Z., Zhang, X., Ruan, D., Xu, G. (2009). A Maximum Class Distance Support Vector Machine-Based Algorithm for Recursive Dimension Reduction. In: Yu, W., He, H., Zhang, N. (eds) Advances in Neural Networks – ISNN 2009. ISNN 2009. Lecture Notes in Computer Science, vol 5552. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-01510-6_29
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DOI: https://doi.org/10.1007/978-3-642-01510-6_29
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-01509-0
Online ISBN: 978-3-642-01510-6
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