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

Authors:
Jun Luo;Bao-Liang Lu
Affiliations:
Department of Computer Science and Engineering, Shanghai Jiao Tong University, Shanghai, China;Department of Computer Science and Engineering, Shanghai Jiao Tong University, Shanghai, China
Venue:
ISNN'06 Proceedings of the Third international conference on Advnaces in Neural Networks - Volume Part II
Year:
2006

Citing 4
Cited 1

Gender Classification with Support Vector Machines

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Gender recognition using a min-max modular support vector machine

ICNC'05 Proceedings of the First international conference on Advances in Natural Computation - Volume Part II
Task decomposition using geometric relation for min-max modular SVMs

ISNN'05 Proceedings of the Second international conference on Advances in Neural Networks - Volume Part I
Task decomposition and module combination based on class relations: a modular neural network for pattern classification

IEEE Transactions on Neural Networks

A Framework for Multi-view Gender Classification

Neural Information Processing

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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.