Independent component analysis: algorithms and applications
Neural Networks
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Learning from examples has a wide number of forms depending on what is to be learned from which available information. One of these form is y= f(x) where the input-output pair (x,y) is the available information and frepresents the process mapping ${\bf x}\in\cal X$ to ${\bf y}\in\cal Y$. In general and for real world problems, it is not reasonnable to expect having the exact representation of f. A fortiori when the dimension of xis large and the number of examples is little. In this paper, we introduce a new model, capable to reduce the complexity of many ill-posedproblems without loss of generality. The underlying Bayesian artifice is presented as an alternative to the currently used frequency approaches which does not offer a compelling criterion in the case of high dimensional problems.