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--- research:largescale [2012/12/24 11:55]
leonb [Approximate Optimization]
+++ research:largescale [2013/02/25 09:57] (current)
leonb [Papers]
@@ Line 70: / Line 70: @@
 complexity of the underlying optimization
 algorithms in non-trivial ways.
 For instance, [[:research:stochastic|Stochastic Gradient Descent (SGD)]] algorithms
-appear to be mediocre optimization algorithms
+appear to be mediocre optimization algorithms and yet are shown to
-and yet are shown to perform extremely well on large-scale learning problems.
+[[:projects/sgd|perform extremely well]] on large-scale learning problems.
@@ Line 81: / Line 80: @@
   * NIPS 2007 tutorial "[[:talks/largescale|Large Scale Learning]]".
+===== Related =====
+   * [[:research:stochastic|Stochastic gradient learning algorithms]]
 ===== Papers =====
@@ Line 91: / Line 93: @@
 </box>
-===== See also =====
+<box 99% orange>
+Léon Bottou and Yann LeCun:  **On-line Learning for Very Large Datasets**,  //Applied Stochastic Models in Business and Industry//, 21(2):137-151, 2005.
-  * [[stochastic|Learning with Stochastic Gradient Descent]].
+[[:papers/bottou-lecun-2004a|more...]]
+</box>
+<box 99% orange>
+Léon Bottou:  **Online Algorithms and Stochastic Approximations**,  //Online Learning and Neural Networks//, Edited by David Saad, Cambridge University Press, Cambridge, UK, 1998.
+[[:papers/bottou-98x|more...]]
+</box>
+<box 99% blue>
+Léon Bottou:  //**Une Approche théorique de l'Apprentissage Connexionniste: Applications à la Reconnaissance de la Parole**//, Orsay, France, 1991.
+[[:papers/bottou-91a|more...]]
+</box>

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