title: Multiple model regression estimation.
abstract: This paper presents a new learning formulation for multiple model estimation (MME). Under this formulation, training data samples are generated by several (unknown) statistical models. Hence, most existing learning methods (for classification or regression) based on a single model formulation are no longer applicable. We describe a general framework for MME. Then we introduce a constructive support vector machine (SVM)-based methodology for multiple regression estimation. Several empirical comparisons using synthetic and real-life data sets are presented to illustrate the proposed approach for multiple model regression formulation.
authors.affiliation: Department of Electrical and Computer Engineering, University of Minnesota, Minneapolis, MN 55455 USA. cherkass@ece.umn.edu
authors.forename: Vladimir
authors.initials: V
authors.lastname: Cherkassky
authors.forename: Yunqian
authors.initials: Y
authors.lastname: Ma
mesh_terms: D000465:Algorithms; D001185:Artificial Intelligence; D016000:Cluster Analysis; D003198:Computer Simulation; D003205:Computing Methodologies; D008954:Models, Biological; D015233:Models, Statistical; D009716:Numerical Analysis, Computer-Assisted; D010363:Pattern Recognition, Automated / Q000379:methods*; D013269:Stochastic Processes