8] T. Poggio and F. Girosi. Regularization Algorithms for Learning That Are Equivalent to Multilayer
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Incipient fault diagnosis of chemical processes via artiicial neural networks. 23 investigate the eeect of modeling uncertainties on the performance of the FDA system. of faults in a multi-loop launch vehicle guidance and control system. Fault diagnosis in dynamic systems using analytical and knowledge-based redundancy | a survey and some new results.
منابع مشابه
Regularization algorithms for learning that are equivalent to multilayer networks.
Learning an input-output mapping from a set of examples, of the type that many neural networks have been constructed to perform, can be regarded as synthesizing an approximation of a multidimensional function (that is, solving the problem of hypersurface reconstruction). From this point of view, this form of learning is closely related to classical approximation techniques, such as generalized ...
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Learning an input-output mapping from a set of examples can be regarded as synthesizing an approximation of a multi-dimensional function. From this point of view, this form of learning is closely related to regularization theory, and we have previously shown (Poggio and Girosi, 1990a, 1990b) the equivalence between reglilari~at.ioll and a. class of three-layer networks that we call regularizati...
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Learning a map from an input set to an output set is similar to the problem of reconstructing hypersurfaces from sparse data (Poggio and Girosi, 1990). In this framework, we discuss the problem of automatically selecting "minimal" surface data. The objective is to be able to approximately reconstruct the surface from the selected sparse data. We show that this problem is equivalent to the one o...
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