<abstract xmlns="http://www.w3.org/1999/xhtml"><p> 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.</p></abstract>

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.

Pattern Synthesis Using Multiple Kernel Learning for Efficient SVM Classification

School of Computing Science and Engineering, VIT University, Vellore-632014

School of Information Technology and Engineering, VIT University, Vellore-632 014

Department of Computer Science and Automation, IISc, Bangalore-12

Cybernetics and Information Technologies

Support Vector Machines (SVMs) have gained prominence because of their high generalization ability for a wide range of applications. However, the size of the...

Pattern Synthesis Using Multiple Kernel Learning for Efficient SVM Classification

Online veröffentlicht: 22. März 2013

Seitenbereich: 77 - 94

DOI: https://doi.org/10.2478/cait-2012-0032

Schlüsselwörter
SVM classifier, curse of dimensionality, synthetic patterns, multiple kernel learning

This content is open access.

Pattern Synthesis Using Multiple Kernel Learning for Efficient SVM Classification

Online veröffentlicht: 22. März 2013

Seitenbereich: 77 - 94

DOI: https://doi.org/10.2478/cait-2012-0032

SchlüsselwörterSVM classifier, curse of dimensionality, synthetic patterns, multiple kernel learning

This content is open access.

Schlüsselwörter
SVM classifier, curse of dimensionality, synthetic patterns, multiple kernel learning