Title :
Support vectors selection by linear programming
Author :
Kecman, Vojislav ; Hadzic, Ivana
Author_Institution :
Dept. of Mech. Eng., Auckland Univ., New Zealand
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
Keywords :
function approximation; learning (artificial intelligence); linear programming; neural nets; pattern classification; statistical analysis; basis functions; function approximation; learning from experimental data; learning machine; linear programming; neural network; nonlinear regression; pattern classification; pattern recognition; support vector machine; support vector selection; Function approximation; Linear programming; Machine learning; Mechanical engineering; Minimization methods; Neural networks; Pattern recognition; Quadratic programming; Support vector machine classification; Support vector machines;
Conference_Titel :
Neural Networks, 2000. IJCNN 2000, Proceedings of the IEEE-INNS-ENNS International Joint Conference on
Conference_Location :
Como
Print_ISBN :
0-7695-0619-4
DOI :
10.1109/IJCNN.2000.861456
