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Neural Processing Letters, Vol. 9, No. 3. (1 June 1999), pp. 293-300, doi:10.1023/a:1018628609742 Key: citeulike:2882650
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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.
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