assignments:assignment2

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The variable $\eta$ plays the role of the learning rate $\eta$ employed in the perceptron algorithm and $\delta \alpha$ is the proposed magnitude of change in $\alpha_i$. | The variable $\eta$ plays the role of the learning rate $\eta$ employed in the perceptron algorithm and $\delta \alpha$ is the proposed magnitude of change in $\alpha_i$. | ||

We note that the adatron tries to maintain a //sparse// representation in terms of the training examples by keeping many $\alpha_i$ equal to zero. The adatron converges to a special case of the SVM algorithm that we will learn later in the semester; this algorithm tries to maximize the margin with which each example is classified, which is captured by the variable $\gamma$ in the algorithm (notice that the magnitude of change proposed for each $\alpha_i$ becomes smaller as the margin increases towards 1). | We note that the adatron tries to maintain a //sparse// representation in terms of the training examples by keeping many $\alpha_i$ equal to zero. The adatron converges to a special case of the SVM algorithm that we will learn later in the semester; this algorithm tries to maximize the margin with which each example is classified, which is captured by the variable $\gamma$ in the algorithm (notice that the magnitude of change proposed for each $\alpha_i$ becomes smaller as the margin increases towards 1). | ||

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+ | **Note:** if you observe an overflow issues in running the adatron, add an upper bound on the value of $\alpha_i$. | ||

Here's what you need to do: | Here's what you need to do: |

assignments/assignment2.txt ยท Last modified: 2016/09/14 09:38 by asa

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