Support vectors selection by linear programming

Abstract

A linear programming (LP) based method is proposed for learning from experimental data in solving the nonlinear regression and classification problems. LP controls both the number of basis functions in a neural network (i.e., support vector machine) and the accuracy of the learning machine. Two different methods are suggested in regression and their equivalence is discussed. Examples of function approximation and classification (pattern recognition) illustrate the efficiency of the proposed method.