Learning with generalization capability by kernel methods of bounded complexity
Journal of Complexity
IEEE Transactions on Information Theory
Sequential greedy approximation for certain convex optimization problems
IEEE Transactions on Information Theory
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The optimization problems associated with supervised learning from data by the weight-decay and related techniques are described. Suboptimal solutions expressed by connectionistic models with a given number of computational units are investigated. In the final part of the paper, improvements of some estimates obtained in [1] for the same optimization problems are given.