Real-time sufficient dimension reduction through principal least squares support vector machines

Sufficient Dimension Reduction via Principal L q Support Vector Machine ∗

Principal weighted support vector machines for sufficient dimension reduction in binary classification

Sufficient dimension reduction is popular for reducing data dimensionality without stringent model assumptions. However, most existing methods may work poorly for binary classification. For example, sliced inverse regression (Li, 1991) can estimate at most one direction if the response is binary. In this paper we propose principal weighted support vector machines, a unified framework for linear...

Fuzzy Least Squares Twin Support Vector Machines

Least Squares Twin Support Vector Machine (LSTSVM) is an extremely efficient and fast version of SVM algorithm for binary classification. LSTSVM combines the idea of Least Squares SVM and Twin SVM in which two nonparallel hyperplanes are found by solving two systems of linear equations. Although, the algorithm is very fast and efficient in many classification tasks, it is unable to cope with tw...

Least Squares Support Vector Machines: an Overview

Support Vector Machines is a powerful methodology for solving problems in nonlinear classification, function estimation and density estimation which has also led recently to many new developments in kernel based learning in general. In these methods one solves convex optimization problems, typically quadratic programs. We focus on Least Squares Support Vector Machines which are reformulations t...

Componentwise Least Squares Support Vector Machines

This chapter describes componentwise Least Squares Support Vector Machines (LS-SVMs) for the estimation of additive models consisting of a sum of nonlinear components. The primal-dual derivations characterizing LS-SVMs for the estimation of the additive model result in a single set of linear equations with size growing in the number of data-points. The derivation is elaborated for the classific...