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Bayesian approach to feature selection and parameter tuning for support vector machine classifiers

✍ Scribed by Carl Gold; Alex Holub; Peter Sollich

Publisher: Elsevier Science
Year: 2005
Tongue: English
Weight: 192 KB
Volume: 18
Category: Article
ISSN: 0893-6080
DOI: 10.1016/j.neunet.2005.06.044

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## Abstract Classification algorithms suffer from the curse of dimensionality, which leads to overfitting, particularly if the problem is over‐determined. Therefore it is of particular interest to identify the most relevant descriptors to reduce the complexity. We applied Bayesian estimates to mode