Ruby SVM |
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SVM means "Support Vector Machine", a general-purpose machine learning algorithm. SVMs deliver state-of-the-art performance in real-world applications such as text categorization, hand-written character recognition, image classification, and bioinformatics. Developed by Vladimir Vapnik, SVMs perform the Structural Risk Minimization (SRM) principle, which minimizes an upper bound on the generalization error, as opposed to Empirical Risk Minimization (ERM) which minimizes the error on training data. It is this difference which equips SVMs with a greater potential to generalize. LIBSVM, developed by Chih-Chung Chang and Chih-Jen Lin, is an integrated software for analyzing many interesting problems in supervised classification or regression frameworks. |
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