SVM-based Learning for Multiple Model Estimation

This paper presents new constructive learning methodology for multiple model estimation. Under multiple model formulation, training data are generated by several (unknown) statistical models, so existing learning methods (for classification or regression) based on a single model formulation are no longer applicable. We describe general framework for multiple model estimation using SVM methodology. The proposed constructive methodology is analyzed in detail for regression formulation. We also present several empirical examples for multiple-model regression formulation. These empirical results illustrate advantages of the proposed multiple model estimation approach.