Regularized Least Squares Piecewise Multi-classification Machine
نویسنده
چکیده
This paper presents a Tikhonov regularization based piecewise classification model for multi-category discrimination of sets or objects. The proposed model includes a linear classification and nonlinear kernel classification model formulation. Advantages of the regularized multi-classification formulations include its ability to express a multi-class problem as a single and unconstrained optimization problem, its ability to derive explicit expressions for the classification weights of the classifiers as well as its computational tractability in providing the optimal classification weights for multi-categorical separation. Computational results are also provided to validate the functionality of the classification models using three data sets (GPA, IRIS, and WINE data). Key-Words: Piecewise, multi-class, multi-category discrimination, least squares, linear classifiers, nonlinear classifiers, linear system of equations
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