乐胖代购免代理版

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To this end, several first-order and second-order algorithms have been proposed in the literature. This paper provides an overview of approaches to backpropagation training, emphazing on first-order adaptive learning algorithms that build on the theory of nonlinear optimization, and proposes a framework for their analysis in the context of deterministic optimization.<\/jats:p>","DOI":"10.1142\/s0218127406015805","type":"journal-article","created":{"date-parts":[[2006,9,8]],"date-time":"2006-09-08T18:41:22Z","timestamp":1157740882000},"page":"1929-1950","source":"Crossref","is-referenced-by-count":16,"title":["ADAPTIVE ALGORITHMS FOR NEURAL NETWORK SUPERVISED LEARNING: A DETERMINISTIC OPTIMIZATION APPROACH"],"prefix":"10.1142","volume":"16","author":[{"given":"GEORGE D.","family":"MAGOULAS","sequence":"first","affiliation":[{"name":"School of Computer Science and Information Systems, Birkbeck College, University of London, Malet Street, London WC1E 7HX, UK"}]},{"given":"MICHAEL N.","family":"VRAHATIS","sequence":"additional","affiliation":[{"name":"Computational Intelligence Laboratory, Department of Mathematics, University of Patras Artificial Intelligence Research Center, University of Patras, GR-26110 Patras, Greece"}]}],"member":"219","published-online":{"date-parts":[[2011,11,20]]},"reference":[{"key":"rf1","unstructured":"Y. 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