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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.
+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]]