ADAPTIVE LEARNING MACHINES FOR NONLINEAR CLASSIFICATION AND BAYESIAN INFORMATION CRITERIA

Regularization is a well-known method for the treatment of mathematically illposed problems. By using the method of regularization, we propose a new machine learning algorithm, adaptive learning machine, to classify the high-dimensional data with complex structure. A crucial issue in the model constructing process is the choice of a suitable model among candidates. We present a Bayesian information criterion to evaluate models estimated by regularization. Real data analysis and Monte Carlo experiments show that our proposed method performs well in various situations.