Fuzzy regression by asymmetric support vector machines
Chih-Chia Yao, Pao-Ta Yu · 2005
Summary form only given. The paper presents a modified framework of support vector machines, called asymmetric support vector machines (ASVMs), which is designed to evaluate the functional relationship for fuzzy linear and nonlinear regression models. In earlier works, to cope with different types of input-output patterns, strong assumptions were made regarding linear fuzzy regression models with symmetric and asymmetric triangular fuzzy coefficients. However, the nonlinear fuzzy regression model has received relatively little attention, with such models having certain limitations. This study modifies the framework of support vector machines in order to overcome these limitations. The principle of ASVMs is to join an orthogonal vector to a weight vector in order to rotate the support hyperplanes. The supreme merits of the proposed model are its simplicity, understandability and effectiveness. Consequently, experimental results and comparisons are given to demonstrate that the basic idea underlying ASVMs can be effectively used for parameter estimation.