Titlebook: Learning to Classify Text Using Support Vector Machines; Thorsten Joachims Book 2002 Springer Science+Business Media New York 2002 Support

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Transductive Text Classification service, for example, requiring a hundred days’ worth of training data is unlikely to please even the most patient users. The work presented in the following tackles the problem of learning from small training samples by taking a . [Vapnik, 1998], instead of an inductive approach. In the inductive

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Training Inductive Support Vector Machineshe 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 Newto

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Training Transductive Support Vector Machines training examples is small and the test set is large, a transductive SVM (TSVM) can offer a substantial benefit over an inductive SVM. However, the problem of computational efficiency in training transductive SVMs has not been considered yet.

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