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Abstract: We provide an RKHS based inverse problem formulation[15] for analytically deriving the optimal function approximation when probabilistic information about the underlying regression is available in terms of the associated correlation functions as used in [9, 8]. On the lines of Poggio and Girosi[9], we show that this solution can be sparsified using principles of SVM and provide an implementation of this sparsification using a novel, conceptually simple and robust gradient based sequential... (Update)
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BibTeX entry: (Update)
@misc{ vijayakumar-gradient,
author = "Sethu Vijayakumar and Si Wu",
title = "A Gradient Based Technique for Generating Sparse Representation in Function
Approximation",
url = "citeseer.ist.psu.edu/vijayakumar99gradient.html" }
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524 Support vector networks - Cortes, Vapnik - 1995
412 Journal of Cognitive Neuroscience (context) - Turk, Pentland et al. - 1991
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55 Regression and the Moore-Penrose Pseudoinverse (context) - Albert - 1972
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