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 linearâ€¦ (More)

Least squares support vector machines (LS-SVM) is an SVM version which involves equality instead of inequality constraints and works with a least squares cost function. In this way, the solutionâ€¦ (More)

In Support Vector Machines (SVMs), the solution of the classification problem is characterized by a (convex) quadratic programming (QP) problem. In a modified version of SVMs, called Least Squaresâ€¦ (More)

The Bayesian evidence framework has been successfully applied to the design of multilayer perceptrons (MLPs) in the work of MacKay. Nevertheless, the training of MLPs suffers from drawbacks like theâ€¦ (More)

In this paper time-delay effects on the masterâ€“slave synchronization scheme are investigated. Sufficient conditions for masterâ€“slave synchronization of Lurâ€™e systems are presented for a knownâ€¦ (More)

In least squares support vector machines (LS-SVM's) for function estimation Vapnik's-insensitive loss function has been replaced by a cost function which corresponds to a form of ridge regression. Inâ€¦ (More)

In this paper we present an extension of least squares support vector machines (LS-SVM's) to the multiclass case. While standard SVM solutions involve solving quadratic or linear programmingâ€¦ (More)

Support vector machines have been very successful in pattern recognition and function estimation problems. In this paper we introduce the use of least squares support vector machines (LS-SVM's) forâ€¦ (More)