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Both sides previous revision Previous revision 2012/12/16 23:44 machacek 2012/12/16 23:40 machacek 2012/12/16 23:02 machacek 2012/12/16 22:40 machacek 2012/12/16 21:59 machacek 2012/12/16 21:34 machacek 2012/12/16 21:32 machacek 2012/12/16 21:07 machacek 2012/12/16 17:43 machacek 2012/12/16 17:43 machacek 2012/12/16 17:37 machacek 2012/12/16 17:36 machacek 2012/12/16 17:08 machacek 2012/12/16 16:46 machacek 2012/12/16 16:25 machacek 2012/12/16 16:25 machacek 2012/12/16 16:04 machacek 2012/12/16 15:56 machacek 2012/12/16 15:39 machacek 2012/12/16 15:25 machacek 2012/12/16 15:23 machacek vytvořeno Go Next revision		Previous revision 2012/12/16 23:44 machacek 2012/12/16 23:40 machacek 2012/12/16 23:02 machacek 2012/12/16 22:40 machacek 2012/12/16 21:59 machacek 2012/12/16 21:34 machacek 2012/12/16 21:32 machacek 2012/12/16 21:07 machacek 2012/12/16 17:43 machacek 2012/12/16 17:43 machacek 2012/12/16 17:37 machacek 2012/12/16 17:36 machacek 2012/12/16 17:08 machacek 2012/12/16 16:46 machacek 2012/12/16 16:25 machacek 2012/12/16 16:25 machacek 2012/12/16 16:04 machacek 2012/12/16 15:56 machacek 2012/12/16 15:39 machacek 2012/12/16 15:25 machacek 2012/12/16 15:23 machacek vytvořeno Go
courses:rg:2012:distributed-perceptron [2012/12/16 23:40] machacek		courses:rg:2012:distributed-perceptron [2012/12/16 23:44] (current) machacek
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	=== 4.1 Parameter Mixing ===		=== 4.1 Parameter Mixing ===
		+
		+	* First attempt to map-reduce algorithm
		+	* Divide the training data into shards
		+	* In MAP step, train a perceptron on each shard
		+	* In REDUCE step, average the trained weight vectors
	=== 4.2 Iterative Parameter Mixing ===		=== 4.2 Iterative Parameter Mixing ===

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