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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

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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.

Metadaten
Titel
Training Inductive Support Vector Machines
verfasst von
Thorsten Joachims
Copyright-Jahr
2002
Verlag
Springer US
DOI
https://doi.org/10.1007/978-1-4615-0907-3_8

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