Comparisons of QP and LP Based Learning from Empirical Data

Authors:
Vojislav Kecman;Tiru Arthanari
Affiliations:
-;-
Venue:
Proceedings of the 14th International conference on Industrial and engineering applications of artificial intelligence and expert systems: engineering of intelligent systems
Year:
2001

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Abstract

The quadratic programming (QP) and the linear programming (LP) based method are recently the most popular learning methods from empirical data. Support vector machines (SVMs) are the newest models based on QP algorithm in solving the nonlinear regression and classification problems. The LP based learning also controls both the number of basis functions in a neural network (i.e., support vector machine) and the accuracy of learning machine. Both methods result in a parsimonious network. This results in data compression. Two different methods are compared in terms of number of SVs (possible compression achieved) and in generalization capability (i.e., error on unseen data).