Bitte verwenden Sie diesen Link, um diese Publikation zu zitieren, oder auf sie als Internetquelle zu verweisen: https://hdl.handle.net/10419/77178 
Autor:innen: 
Erscheinungsjahr: 
1998
Schriftenreihe/Nr.: 
Technical Report No. 1998,28
Verlag: 
Universität Dortmund, Sonderforschungsbereich 475 - Komplexitätsreduktion in Multivariaten Datenstrukturen, Dortmund
Zusammenfassung: 
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, there are many issues to be considered in designing an SVM learner. In particular, for large learning tasks with many training examples on the shelf optimization techniques for general quadratic programs quickly become intractable in their memory and time requirements. SVM light is an implementation of an SVM learner which addresses the problem of large tasks. This chapter presents algorithmic and computational results developed for SVM light V 2.0, which make large-scale SVM training more practical. The results give guidelines for the application of SVMs to large domains.
Dokumentart: 
Working Paper

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