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assignments:assignment2 [2016/09/06 09:54]
asa
assignments:assignment2 [2016/09/14 09:38]
asa [Assignment 2]
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 ====== Assignment 2 ====== ====== Assignment 2 ======
  
-Due date:  Friday 9/16 at 11:59pm+Due date:  Friday 9/17 at 11:59pm
  
 ==== Datasets ==== ==== Datasets ====
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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).
 +
 +**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