Least Squares Support Vector Machine Classifiers View Full Text


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Article Info

DATE

1999-06

AUTHORS

J.A.K. Suykens, J. Vandewalle

ABSTRACT

In this letter we discuss a least squares version for support vector machine (SVM) classifiers. Due to equality type constraints in the formulation, the solution follows from solving a set of linear equations, instead of quadratic programming for classical SVM's. The approach is illustrated on a two-spiral benchmark classification problem.

PAGES

293-300

References to SciGraph publications

Identifiers

URI

http://scigraph.springernature.com/pub.10.1023/a:1018628609742

DOI

http://dx.doi.org/10.1023/a:1018628609742

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1025353016


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