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courses:rg:2012:distributed-perceptron [2012/12/16 17:43] machacek |
courses:rg:2012:distributed-perceptron [2012/12/16 21:59] machacek |
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| ==== 3 Structured Perceptron ==== | ==== 3 Structured Perceptron ==== | ||
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| + | * In unstructured perceptron, you are trying to separate two sets 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:// | ||
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| + | * Structured (or multiclass) perceptron is generalization of the unstructured perceptron. See figure 1 in the paper for the algorithm. | ||
| ==== 4 Distributed Structured Perceptron ==== | ==== 4 Distributed Structured Perceptron ==== | ||
| - | === 4.1 Parameter Mixing ===+ | + | * Motivation: There is no straightforward way to make the standard perceptron algorithm parallel. |
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| + | === 4.1 Parameter Mixing === | ||
| === 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 ==== | ||