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courses:rg:2012:distributed-perceptron [2012/12/16 15:56]
machacek 		courses:rg:2012:distributed-perceptron [2012/12/16 17:08]
machacek
Line 44: Line 44:
 f = ? f = ?
 w = [?, ?] w = [?, ?]
 +
 +**Answer 1:**
 +
 +f(**x**,y) = (y == 0) ? (**x**, 0,0,...,0) : (0,0,...,0,**x**)
 +
 +**Answer 2:**
 +
 +Acording to English [[http://en.wikipedia.org/wiki/Perceptron#Multiclass_perceptron|Wikipedia]]: This multiclass formulation reduces to the original perceptron when **x** is a real-valued vector, y is chosen from {0,1}, and f(**x**,y) = y * **x**.
 +
 +However, I would say, that this holds only for activation treshold = 0. Therefore, this formula cannot be used to compute example from Question 1.
 +
  
 ==== 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)?
 +
 +
 +**Answer:**
 +
 +  * 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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