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courses:rg:2012:distributed-perceptron [2012/12/16 16:46]
machacek
courses:rg:2012:distributed-perceptron [2012/12/16 17:08]
machacek
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 ==== Question 3 ==== ==== Question 3 ====
 In figure 4, why do you think that the F-measure for Regular Perceptron (first column) learned by the Serial (All Data) algorithm is worse than the Parallel (Iterative Parametere Mix)? In figure 4, why do you think that the F-measure for Regular Perceptron (first column) learned by the Serial (All Data) algorithm is worse than the Parallel (Iterative Parametere Mix)?
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 +**Answer:**
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 +  * Iterative Parameter Mixing is just a form of parameter averaging, which has the same effect as the averaged perceptron.
 +    * F-measures for seral (All Data) and Paralel (Iterative Parameter Mix) are very similar in the second column. It is because the both methods are already averaged.
 +  * Bagging like effect
  
 ==== Question 4 ==== ==== Question 4 ====

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