Gender Recognition Using a Min-Max Modular Support Vector Machine with Equal Clustering

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