Least Squares Support Vector Machine Classifiers

In machine learning support vector machines svms also support vector networks are supervised learning models with associated learning algorithms that analyze data used for classification and regression analysisthe support vector machine svm algorithm is a popular machine learning tool that offers solutions for both classification and regression problems. In this paper multi label classification problem is regarded as an unbalanced multi task learning problem. Due to equality type constraints in the formulation the solution follows from solving a set of linear equations instead of quadratic programming for classical svms.
least squares support vector machine classifiers

In this version one finds the solution by solving a set of linear equations instead of a convex quadratic programming problem for classical svms.

Least squares support vector machine classifiers. The approach is illustrated on a two spiral benchmark classification problem. Least squares support vector machines are least squares versions of support vector machines which are a set of related supervised learning methods that analyze data and recognize patterns and which are used for classification and regression analysis. Least squares support vector machine classifiers 295 subject to 4. Least squares support vector machine ls svm which has been successfully applied to a number of real problems of classification and function estimation is least squares version of svm and was.

In this letter we discuss a least squares version for support vector machine svm classifiers. The proposed als svm can also be regarded as an extension to the least squares support vector machine ls svm suykens and vandewalle 1999 suykens et al 2002b. Ls svms are a class of kernel. Therefore one constructs the lagrangian l1wbx kia kn k dj1wx k p n kd1 a kfy ktw t0x kcbu 1 cx kg p n kd1 n kx k 6 by introducing lagrange multipliers a.

Multi task least squares support vector machine mtls svm is generalized for this problem renamed as multi label ls svm ml 2 s svm. Due to equality type constraints in the formulation the solution follows from solving a set of. In this letter we discuss a least squares version for support vector machine svm classifiers. The approach is illustrated on a two spiral benchmark classification problem.

In this letter we discuss a least squares version for support vector machine svm classifiers. When no bias term is used the ls svm in the primal space corresponds to ridge regression as discussed by van gestel et al. Least squares support vector machine classifiers. Least squares svm classifiers were proposed by suykens and vandewalle.

Due to equality type constraints in the formulation the solution follows from solving a set of linear equations instead of quadratic programming for classical svms.