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Hi I am performing SVM classification using SMO, in which my kernel is RBF, now I want to select c and sigma values, using grid search and cross validation, I am new to kernel functions, please help, in step by step process
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The gamma parameters can be seen as the inverse of the radius of influence of samples selected by the model as support vectors. The C parameter trades off correct classification of training examples against maximization of the decision function's margin.
I always think of c as the cost of misclassification (easy to remember by c in classification). In that way higher c means high cost of misclassification, leading to the algorithm trying to perfectly separate all data points.
C parameter in SVM is Penalty parameter of the error term. You can consider it as the degree of correct classification that the algorithm has to meet or the degree of optimization the the SVM has to meet. For greater values of C, there is no way that SVM optimizer can misclassify any single point.
The gamma parameter defines how far the influence of a single training example reaches, with low values meaning 'far' and high values meaning 'close'. The lower values of gamma result in models with lower accuracy and the same as the higher values of gamma.
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Read A Practical Guide to Support Vector Classication by Chih-Wei Hsu, Chih-Chung Chang, and Chih-Jen. They address this exact issue and explain methods for performing a grid-search for parameter selection. http://www.csie.ntu.edu.tw/~cjlin/papers/guide/guide.pdf
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