Multiple model classification using SVM-based approach

We propose a new method for nonlinear classification using several simple (linear) classifiers. The approach is based on a new formulation of the learning problem called multiple model estimation [V. Cherkassky and Y. Ma, 2002]. The paper describes practical implementation of this approach using an appropriate modification of standard SVM classification algorithm. Several empirical comparisons presented in this paper indicate that the proposed multiple model classification (MMC) method (using linear component models) yields better (or similar) prediction accuracy than standard nonlinear SVM classifiers. However, the main practical advantage of MMC method is that it does not require heuristic tuning of nonlinear SVM parameters (such as selection of kernel type, regularization parameter) in order to achieve good classification accuracy.