Regularization Based Classification Models

This paper presents Tikhonov regularization based classification models for binary discrimination of sets or objects. The proposed models include a linear classification, nonlinear kernel classification and a reduced kernel classification model in the case of large scale problems. For the reduced kernel formulation, the dimension reduction of the kernel matrix is achieved by selecting random subsets of the training set. Advantages of the regularized classification formulations include explicit expressions for the classification weights of the classifier as well as its computational tractability in providing the optimal classification weights for two-class separation problems. Computational results are also provided for validation of the classification models.