Gaining insights into support vector machine pattern classifiers using projection-based tour methods

Doina Caragea, Dianne Cook, Vasant Honavar

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

38 Scopus citations

Abstract

This paper discusses visual methods that can be used to understand and interpret the results of classification using support vector machines (SVM) on data with continuous real-valued variables. SVM induction algorithms build pattern classifiers by identifying a maximal margin separating hyperplane from training examples in high dimensional pattern spaces or spaces induced by suitable nonlinear kernel transformations over pattern spaces. SVM have been demonstrated to be quite effective in a number of practical pattern classification tasks. Since the separating hyperplane is defined in terms of more than two variables it is necessary to use visual techniques that can navigate the viewer through high-dimensional spaces. We demonstrate the use of projection-based tour methods to gain useful insights into SVM classifiers with linear kernels on 8-dimensional data.

Original languageEnglish (US)
Title of host publicationProceedings of the Seventh ACM SIGKDD International Conference on Knowledge Discovery and Data Mining
EditorsF. Provost, R. Srikant, M. Schkolnick, D. Lee
PublisherAssociation for Computing Machinery (ACM)
Pages251-256
Number of pages6
ISBN (Print)158113391X, 9781581133912
DOIs
StatePublished - 2001
EventProceedings of the Seventh ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD-2001) - San Francisco, CA, United States
Duration: Aug 26 2001Aug 29 2001

Publication series

NameProceedings of the Seventh ACM SIGKDD International Conference on Knowledge Discovery and Data Mining

Other

OtherProceedings of the Seventh ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD-2001)
CountryUnited States
CitySan Francisco, CA
Period8/26/018/29/01

All Science Journal Classification (ASJC) codes

  • Engineering(all)

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