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courses:rg:2012:distributed-perceptron [2012/12/16 21:07]
machacek
courses:rg:2012:distributed-perceptron [2012/12/16 23:44] (current)
machacek
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-====== Distributed Training Strategies for the Structured Perceptron - RG report ======+====== Distributed Training Strategies for the Structured Perceptron - RG report - UNDER CONSTRUCTION ======
  
 ===== Presentation ===== ===== Presentation =====
  
 ==== 3 Structured Perceptron ==== ==== 3 Structured Perceptron ====
 +
 +  * In unstructured perceptron, you are trying to separate two sets of with hyperplane. See Question 1 for the algorithm. In training phase, you iterate your training data and adjust the hyperplane every time you make a mistake. [[http://www.youtube.com/watch?v=vGwemZhPlsA|Youtube Example]]
 +
 +  * Structured (or multiclass) perceptron is generalization of the unstructured perceptron. See figure 1 in the paper for the algorithm.
 +  * You can use any structured input x (not just vector, sentence for example) and any structured output y (not just binary value, parse tree for example)
 +  * You need to have fuction f(x,y) which returns feature representation of candidate input-output pair
 +  * Using the Theorem 1, you can bound the number of mistakes made during the training
 +    * The computational time is therefore also bounded.
 +    * This holds only for linearly separable sets.
 +  * Other remarks and discussed issues
 +    * The perceptron training algorithm does not always return the same weights (unlike maximal margin). It depends on order of training data.
 +    * How the inference is done in the difficult tasks like parsing? Iterating all possible y? Approximation?
  
 ==== 4 Distributed Structured Perceptron ==== ==== 4 Distributed Structured Perceptron ====
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 === 4.1 Parameter Mixing === === 4.1 Parameter Mixing ===
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 +  * 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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 w = [0, 0.6] w = [0, 0.6]
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 +  
  
 ==== Question 2 ==== ==== Question 2 ====

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