Abstract
Through task decomposition and module combination, min-max modular support vector machines (M3-SVMs) can be successfully used for different pattern classification tasks. Based on an equal clustering algorithm, M3-SVMs can divide the training data set of the original problem into several subsets with nearly equal number of samples, and combine them to a series of balanced subproblems which can be trained more efficiently and effectively. In this paper, we explore the use of M3-SVMs with equal clustering method in gender recognition. The experimental results show that M3-SVMs with equal clustering method can be successfully used for gender recognition and make the classification more efficient and accurate.
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Luo, J., Lu, BL. (2006). Gender Recognition Using a Min-Max Modular Support Vector Machine with Equal Clustering. In: Wang, J., Yi, Z., Zurada, J.M., Lu, BL., Yin, H. (eds) Advances in Neural Networks - ISNN 2006. ISNN 2006. Lecture Notes in Computer Science, vol 3972. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11760023_31
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DOI: https://doi.org/10.1007/11760023_31
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-34437-7
Online ISBN: 978-3-540-34438-4
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