A Nondeterministic Minimization Algorithm
| dc.date.accessioned | 2004-10-04T15:31:26Z | |
| dc.date.accessioned | 2018-11-24T10:14:57Z | |
| dc.date.available | 2004-10-04T15:31:26Z | |
| dc.date.available | 2018-11-24T10:14:57Z | |
| dc.date.issued | 1990-09-01 | en_US |
| dc.identifier.uri | http://hdl.handle.net/1721.1/6560 | |
| dc.identifier.uri | http://repository.aust.edu.ng/xmlui/handle/1721.1/6560 | |
| dc.description.abstract | 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, 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. | en_US |
| dc.format.extent | 1240414 bytes | |
| dc.format.extent | 492517 bytes | |
| dc.language.iso | en_US | |
| dc.title | A Nondeterministic Minimization Algorithm | en_US |
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