Please use this identifier to cite or link to this item: http://dspace.mediu.edu.my:8181/xmlui/handle/1721.1/6560
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dc.creatorCaprile, Bruno-
dc.creatorGirosi, Federico-
dc.date2004-10-04T15:31:26Z-
dc.date2004-10-04T15:31:26Z-
dc.date1990-09-01-
dc.date.accessioned2013-10-09T02:45:58Z-
dc.date.available2013-10-09T02:45:58Z-
dc.date.issued2013-10-09-
dc.identifierAIM-1254-
dc.identifierhttp://hdl.handle.net/1721.1/6560-
dc.identifier.urihttp://koha.mediu.edu.my:8181/xmlui/handle/1721-
dc.descriptionThe 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, 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.-
dc.format1240414 bytes-
dc.format492517 bytes-
dc.formatapplication/postscript-
dc.formatapplication/pdf-
dc.languageen_US-
dc.relationAIM-1254-
dc.titleA Nondeterministic Minimization Algorithm-
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