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--- papers:bottou-murata-2002 [2006/04/18 17:58]
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+++ papers:bottou-murata-2002 [2006/04/20 11:04]
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-===== Stochastic Approximations and Efficient Learning =====
-//Excerpt//:
-The analysis of online algorithms is much more difficult than that of
-ordinary optimization algorithms.  Practical successes in signal
-processing (Widrow and Stearns, 1985) motivated the creation of
-sophisticated mathematical tools known as {\em stochastic
-approximations} (Ljung and Soderstrom, 1983; Benveniste, Metivier and Priouret, 1990)
-[...]
-The first section describes and illustrates a general framework for
-neural network learning algorithms based on stochastic gradient
-descent.  The second section presents stochastic approximation results
-describing the //final phase//.  The third section discusses the
-conceptual aspects of the //search phase// and comments some of the
-newest results.
-<box 99% orange>
-Léon Bottou and Noboru Murata: Stochastic Approximations and Efficient Learning,  //The Handbook of Brain Theory and Neural Networks, Second edition,//, Edited by M. A. Arbib, The MIT Press, Cambridge, MA, 2002.
-[[http://leon.bottou.org/publications/djvu/arbib-2002.djvu|arbib-2002.djvu]]
-[[http://leon.bottou.org/publications/pdf/arbib-2002.pdf|arbib-2002.pdf]]
-[[http://leon.bottou.org/publications/psgz/arbib-2002.ps.gz|arbib-2002.ps.gz]]
-</box>
-  @incollection{bottou-murata-2002,
-    author = {Bottou, L\'{e}on and Murata, Noboru},
-    title = {Stochastic Approximations and Efficient Learning},
-    booktitle = {The Handbook of Brain Theory and Neural Networks, Second edition,},
-    editor = {Arbib, M. A.},
-    publisher = {The MIT Press},
-    address = {Cambridge, MA},
-    year = {2002},
-    url = {http://leon.bottou.org/papers/bottou-murata-2002},
-  }

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