Adaptive color space switching based approach for face tracking
ICONIP'06 Proceedings of the 13th international conference on Neural Information Processing - Volume Part II
A MapReduce-based distributed SVM algorithm for automatic image annotation
Computers & Mathematics with Applications
Detecting RNA sequences using two-stage SVM classifier
LSMS'07 Proceedings of the 2007 international conference on Life System Modeling and Simulation
Parallel multitask cross validation for Support Vector Machine using GPU
Journal of Parallel and Distributed Computing
Fast classification for large data sets via random selection clustering and Support Vector Machines
Intelligent Data Analysis
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Support vector machines (SVMs) have been extensively used. However, it is known that SVMs face difficulty in solving large complex problems due to the intensive computation involved in their training algorithms, which are at least quadratic with respect to the number of training examples. This paper proposes a new, simple, and efficient network architecture which consists of several SVMs each trained on a small subregion of the whole data sampling space and the same number of simple neural quantizer modules which inhibit the outputs of all the remote SVMs and only allow a single local SVM to fire (produce actual output) at any time. In principle, this region-computing based modular network method can significantly reduce the learning time of SVM algorithms without sacrificing much generalization performance. The experiments on a few real large complex benchmark problems demonstrate that our method can be significantly faster than single SVMs without losing much generalization performance.