In this chapter, we revise several methods for SVM model selection, deriving from different approaches: some of them build on practical lines of reasoning but are not fully justified by a theoretical point of view; on the other hand, some methods rely on rigorous theoretical work but are of little help when applied to real-world problems, because the underlying hypotheses cannot be verified or the result of their application is uninformative. Our objective is to sketch some light on these issues by carefully analyze the most well-known methods and test some of them on standard benchmarks to evaluate their effectiveness.
Theoretical and Practical Model Selection Methods for Support Vector Classifiers
ANGUITA, DAVIDE;RIDELLA, SANDRO;
2005-01-01
Abstract
In this chapter, we revise several methods for SVM model selection, deriving from different approaches: some of them build on practical lines of reasoning but are not fully justified by a theoretical point of view; on the other hand, some methods rely on rigorous theoretical work but are of little help when applied to real-world problems, because the underlying hypotheses cannot be verified or the result of their application is uninformative. Our objective is to sketch some light on these issues by carefully analyze the most well-known methods and test some of them on standard benchmarks to evaluate their effectiveness.
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