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Adaptive learning machines for nonlinear classification and Bayesian information criteria. (English) Zbl 1270.68235
Summary: Regularization is a well-known method for the treatment of mathematically ill-posed 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.

68T05 Learning and adaptive systems in artificial intelligence
62H30 Classification and discrimination; cluster analysis (statistical aspects)