Fast model tracking with multiple cameras for augmented reality
Proceedings of the ACM symposium on Virtual reality software and technology
HyperBF networks for real object recognition
IJCAI'91 Proceedings of the 12th international joint conference on Artificial intelligence - Volume 2
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The problem of minimizing a multivariate function is recurrent in many disciplines as Physics, Mathematics, Engeneering and, of course, Computer Science. In this paper we describe a simple nondeterministic algorithm which is based on the idea of adaptive noise, and that proved to be particularly effective in the minimization of a class of multivariate, {\it continuous valued, smooth functions}, associated with some recent extension of regularization theory by Poggio and Girosi (1990). Results obtained by using this method and a more traditional gradient descent technique are also compared.