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Erschienen in:
2016 | OriginalPaper | Buchkapitel
5. Kernel-Based SVM
verfasst von : M. N. Murty, Rashmi Raghava
Erschienen in: Support Vector Machines and Perceptrons
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Abstract
Kernel Support Vector Machine (SVM) is useful to deal with nonlinear classification based on a linear discriminant function in a high-dimensional (kernel) space. Linear SVM is popularly used in applications involving high-dimensional spaces. However, in low-dimensional spaces, kernel SVM is a popular nonlinear classifier. It employs kernel trick which permits us to work in the input space instead of dealing with a potentially high-dimensional, even theoretically infinite dimensional, kernel (feature) space. Also kernel trick has become so popular that it is used in a variety of other pattern recognition and machine learning algorithms.