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You may find $\nu$-svm interesting. It re-parameterises the problem to give bounds on the number of support vectors and the misclassification rate. Google "nu svm", or follow the links at http://stackoverflow.com/questions/11230955/what-is-the-meaning-of-the-nu-parameter-in-scikit-learns-svm-classhttps://stackoverflow.com/questions/11230955/what-is-the-meaning-of-the-nu-parameter-in-scikit-learns-svm-class

You may find $\nu$-svm interesting. It re-parameterises the problem to give bounds on the number of support vectors and the misclassification rate. Google "nu svm", or follow the links at http://stackoverflow.com/questions/11230955/what-is-the-meaning-of-the-nu-parameter-in-scikit-learns-svm-class

You may find $\nu$-svm interesting. It re-parameterises the problem to give bounds on the number of support vectors and the misclassification rate. Google "nu svm", or follow the links at https://stackoverflow.com/questions/11230955/what-is-the-meaning-of-the-nu-parameter-in-scikit-learns-svm-class

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You may find $\nu$-svm interesting. It re-parameterises the problem to give bounds on the number of support vectors and the misclassification rate. Google "nu svm", or follow the links at http://stackoverflow.com/questions/11230955/what-is-the-meaning-of-the-nu-parameter-in-scikit-learns-svm-class