Large margin classifiers

Campbell, C., Cristianini, N., and Smola, A. Query learning with large margin classifiers. International Conference on Machine Learning. Morgan Kaufmann, Stanford, CA, 2000, 8. 

In practice, the soft margin versions of the standard SVM (also known as C-SVM) described in the previous sections often suffer from the following problems. Firstly, there is a problem of how to determine the error penalty parameter C. Although the cross-validation technique can be used to determine this parameter, it is still hard to explain. Secondly, the time taken for a support vector classifier to compute the class of a new sample is proportional to the number of support vectors, so if that number is large, the computation is time-consuming. 

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