Differences
This shows you the differences between two versions of the page.
| Both sides previous revision Previous revision | Next revision Both sides next revision | ||
|
courses:rg:2012:distributed-perceptron [2012/12/16 22:40] machacek |
courses:rg:2012:distributed-perceptron [2012/12/16 23:02] machacek |
||
|---|---|---|---|
| Line 5: | Line 5: | ||
| ==== 3 Structured Perceptron ==== | ==== 3 Structured Perceptron ==== | ||
| - | * 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:// | + | * 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:// |
| * Structured (or multiclass) perceptron is generalization of the unstructured perceptron. See figure 1 in the paper for the algorithm. | * 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) | ||
| ==== 4 Distributed Structured Perceptron ==== | ==== 4 Distributed Structured Perceptron ==== | ||