Gender recognition using a min-max modular support vector machine with equal clustering

Abstract

Through task decomposition and module combination, minmax modular support vector machines (M3-SVMs) can be successfully used for different pattern classification tasks. Based on an equal clustering algorithm, M 3 -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 M 3 -SVMs with equal clustering method in gender recognition. The experimental results show that M 3 -SVMs with equal clustering method can be successfully used for gender recognition and make the classification more efficient and accurate. © Springer-Verlag Berlin Heidelberg 2006.