Characterization of data complexity for SVM methods

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Scopus citations

Abstract

This paper provides new characterization of data complexity for margin-based methods also known as SVMs, kernel methods etc. Under the predictive learning setting, the complexity of a given data set is directly related to model complexity, i.e. the flexibility of a set of admissible models used to describe this data. There are two distinct approaches to model complexity control: traditional model-based where complexity is controlled via parameterization of admissible models, and margin-based where complexity is controlled by the size of margin (in a specially designed empirical loss function). This paper emphasizes the role of margin for complexity control, and proposes a simple index for data complexity suitable for classification and regression problems.

Original languageEnglish (US)
Title of host publicationProceedings of the International Joint Conference on Neural Networks, IJCNN 2005
Pages919-924
Number of pages6
DOIs
StatePublished - Dec 1 2005
EventInternational Joint Conference on Neural Networks, IJCNN 2005 - Montreal, QC, Canada
Duration: Jul 31 2005Aug 4 2005

Publication series

NameProceedings of the International Joint Conference on Neural Networks
Volume2

Other

OtherInternational Joint Conference on Neural Networks, IJCNN 2005
CountryCanada
CityMontreal, QC
Period7/31/058/4/05

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