In this paper, we present a least squares version for support vector machines(SVM)classifiers and functionestimation. Due to equality type constraints in the formulation, the solution follows from solving a set of lin...In this paper, we present a least squares version for support vector machines(SVM)classifiers and functionestimation. Due to equality type constraints in the formulation, the solution follows from solving a set of linear equa-tions, instead of quadratic programming for classical SVM. The approach is illustrated on a two-spiral benchmarkclassification problem. The results show that the LS-SVM is an efficient method for solving pattern recognition.展开更多
文摘In this paper, we present a least squares version for support vector machines(SVM)classifiers and functionestimation. Due to equality type constraints in the formulation, the solution follows from solving a set of linear equa-tions, instead of quadratic programming for classical SVM. The approach is illustrated on a two-spiral benchmarkclassification problem. The results show that the LS-SVM is an efficient method for solving pattern recognition.
