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Both sides previous revision Previous revision Next revision | Previous revision Next revision Both sides next revision | ||
assignments:assignment3 [2013/10/06 15:22] asa |
assignments:assignment3 [2013/10/06 20:51] asa |
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Express the closest centroid algorithm in terms of kernels, i.e. determine how the coefficients $\alpha_i$ will be computed using a given labeled dataset. | Express the closest centroid algorithm in terms of kernels, i.e. determine how the coefficients $\alpha_i$ will be computed using a given labeled dataset. | ||
- | ==== Part 3: Soft-margin for separable data ==== | + | ===== Part 3: Soft-margin SVM for separable data ===== |
Consider training a soft-margin SVM | Consider training a soft-margin SVM | ||
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K_{poly}(\mathbf{x}, \mathbf{x'}) = (1 + \mathbf{x}^T \mathbf{x}') ^{p} | K_{poly}(\mathbf{x}, \mathbf{x'}) = (1 + \mathbf{x}^T \mathbf{x}') ^{p} | ||
$$ | $$ | ||
- | Plot the accuracy of the SVM, measured using the balnced success rate | + | Plot the accuracy of the SVM, measured using the balanced success rate |
as a function of both the soft-margin parameter of the SVM, and the free parameter | as a function of both the soft-margin parameter of the SVM, and the free parameter | ||
of the kernel function. | of the kernel function. |