Abstract
For machine learning of an input-output function f from examples, we show it is possible to define an a priori probability density function on the hypothesis space to represent knowledge of the probability distribution of f, even when the hypothesis space H is large (i.e., nonparametric). This allows extension of maximum a posteriori (MAP) estimation methods nonparametric function estimation. Among other things, the resulting MAPN (MAP for nonparametric machine learning) procedure easily reproduces spline and radial basis function solutions of learning problems.
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© 2005 Springer-Verlag Berlin Heidelberg
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Kon, M., Plaskota, L., Przybyszewski, A. (2005). Machine Learning and Statistical MAP Methods. In: Kłopotek, M.A., Wierzchoń, S.T., Trojanowski, K. (eds) Intelligent Information Processing and Web Mining. Advances in Soft Computing, vol 31. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-32392-9_49
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DOI: https://doi.org/10.1007/3-540-32392-9_49
Publisher Name: Springer, Berlin, Heidelberg
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