Least Squares Support Vector Machine Classifiers

Clicks: 392
ID: 111180
1970
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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.
Reference Key
suykens1970neuralleast Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors J.a.k. Suykens, J. Vandewalle
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Journal neural processing letters
Year 1970
DOI
doi:10.1023/A:1018628609742
URL
https://dx.doi.org/10.1023/A:1018628609742
Keywords
artificial intelligence complex systems computational intelligence

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