Priors Stabilizers and Basis Functions: From Regularization to Radial, Tensor and Additive Splines
Data(s) |
20/10/2004
20/10/2004
01/06/1993
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Resumo |
We had previously shown that regularization principles lead to approximation schemes, as Radial Basis Functions, which are equivalent to networks with one layer of hidden units, called Regularization Networks. In this paper we show that regularization networks encompass a much broader range of approximation schemes, including many of the popular general additive models, Breiman's hinge functions and some forms of Projection Pursuit Regression. In the probabilistic interpretation of regularization, the different classes of basis functions correspond to different classes of prior probabilities on the approximating function spaces, and therefore to different types of smoothness assumptions. In the final part of the paper, we also show a relation between activation functions of the Gaussian and sigmoidal type. |
Formato |
27 p. 768627 bytes 2437996 bytes application/octet-stream application/pdf |
Identificador |
AIM-1430 CBCL-075 |
Idioma(s) |
en_US |
Relação |
AIM-1430 CBCL-075 |
Palavras-Chave | #regularization theory #radial basis functions #additivesmodels #prior knowledge #multilayer perceptrons |