Statistical Likelihood Representations of Prior Knowledge in Machine Learning

M.A. Kon (USA), L. Plaskota (Poland), and A. Przybyszewski (Canada, USA)


machine learning, Bayesian statistics


We show that maximum a posteriori (MAP) statistical methods can be used in nonparametric machine learning problems in the same way as their current applications in parametric statistical problems, and give some examples of applications. This MAPN (MAP for nonparametric ma chine learning) paradigm can also reproduce much more transparently the same results as regularization methods in machine learning, spline algorithms in continuous com plexity theory, and Baysian minimum risk methods.

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