文章基本信息

标题：Pattern Synthesis Using Multiple Kernel Learning for Efficient SVM Classification
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作者：H. Seetha ; R. Saravanan ; M. Narasimha Murty 等
期刊名称：Cybernetics and Information Technologies
印刷版ISSN：1311-9702
电子版ISSN：1314-4081
出版年度：2012
卷号：12
期号：4
出版社：Bulgarian Academy of Science
摘要：Support Vector Machines (SVMs) have gained prominence because of their high generalization ability for a wide range of applications. However, the size of the training data that it requires to achieve a commendable performance becomes extremely large with increasing dimensionality using RBF and polynomial kernels. Synthesizing new training patterns curbs this effect. In this paper, we propose a novel multiple kernel learning approach to generate a synthetic training set which is larger than the original training set. This method is evaluated on seven of the benchmark datasets and experimental studies showed that SVM classifier trained with synthetic patterns has demonstrated superior performance over the traditional SVM classifier.
关键词：SVM classifier; curse of dimensionality; synthetic patterns; multiple;kernel learning.