assignments:assignment2
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assignments:assignment2 [2016/09/06 09:40] asa |
assignments:assignment2 [2016/09/06 09:51] asa |
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| === The adatron === | === The adatron === | ||
| - | before we get to the adatron, we will derive an alternative form of the perceptron algorithm -- the dual perceptron algorithm. All we need to look at is the weight update rule: | + | Before we get to the adatron, we will derive an alternative form of the perceptron algorithm --- the dual perceptron algorithm. All we need to look at is the weight update rule: |
| $$\mathbf{w} \rightarrow \mathbf{w} + \eta y_i \mathbf{x}_i.$$ | $$\mathbf{w} \rightarrow \mathbf{w} + \eta y_i \mathbf{x}_i.$$ | ||
| Line 34: | Line 34: | ||
| where $\alpha_i$ are positive numbers that describe the magnitude of the contribution $\mathbf{x}_i$ is making to the weight vector, and $N$ is the number of training examples. | where $\alpha_i$ are positive numbers that describe the magnitude of the contribution $\mathbf{x}_i$ is making to the weight vector, and $N$ is the number of training examples. | ||
| - | Therefore to initialize $\mathbf{w}$ to 0, we simply initialize $\alpha_i = 0$ for $i = 1,\ldots,N$. For the adatron we'll use an alternative initialization: | + | Therefore to initialize $\mathbf{w}$ to 0, we simply initialize $\alpha_i = 0$ for $i = 1,\ldots,N$. In terms of the variables $\alpha_i$, the perceptron |
| - | $\alpha_i = 1$ for $i = 1,\ldots,N$. | + | |
| - | Now back to the perceptron... in terms of the variables $\alpha_i$, the perceptron | + | |
| update rule becomes: | update rule becomes: | ||
assignments/assignment2.txt · Last modified: 2016/09/14 09:38 by asa
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