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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.

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