2002 | OriginalPaper | Buchkapitel
Training Inductive Support Vector Machines
verfasst von : Thorsten Joachims
Erschienen in: Learning to Classify Text Using Support Vector Machines
Verlag: Springer US
Enthalten in: Professional Book Archive
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Training a support vector machine (SVM) leads to a quadratic optimization problem with bound constraints and one linear equality constraint. Despite the fact that this type of problem is well understood in principle, there are many issues to be considered in designing an SVM learner. In particular, for large learning tasks with many training examples, off-the-shelf optimization techniques for general quadratic programs such as Newton, Quasi Newton, etc., quickly become intractable in their memory and time requirements.