The Entropy Regularization Information Criterion

Part of Advances in Neural Information Processing Systems 12 (NIPS 1999)

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Alex Smola, John Shawe-Taylor, Bernhard Schölkopf, Robert C. Williamson


Effective methods of capacity control via uniform convergence bounds for function expansions have been largely limited to Support Vector ma(cid:173) chines, where good bounds are obtainable by the entropy number ap(cid:173) proach. We extend these methods to systems with expansions in terms of arbitrary (parametrized) basis functions and a wide range of regulariza(cid:173) tion methods covering the whole range of general linear additive models. This is achieved by a data dependent analysis of the eigenvalues of the corresponding design matrix.