Experiential Study of Kernel Functions to Design an Optimized Multi-class SVM

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Manju Bala

Abstract

Support Vector Machine is a powerful classification technique based on the idea of Structural risk minimization. Use of a kernel function enables the curse of dimensionality to be addressed. However, a proper kernel function for a certain problem is dependent on the specific dataset and till now there is no good method on how to choose a kernel function. In this paper, the choice of the kernel function was studied empirically and optimal results were achieved for multi-class SVM by combining several binary classifiers. The performance of the multi-class SVM is illustrated by extensive experimental results which indicate that with suitable kernel and parameters better classification accuracy can be achieved as compared to other methods. The experimental results of three datasets show that Gaussian kernel is not always the best choice to achieve high generalization of classifier although it often the default choice.

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How to Cite
, M. B. (2017). Experiential Study of Kernel Functions to Design an Optimized Multi-class SVM. International Journal on Future Revolution in Computer Science &Amp; Communication Engineering, 3(12), 112–117. Retrieved from http://www.ijfrcsce.org/index.php/ijfrcsce/article/view/376
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