cleaning up svm slides
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@@ -71,11 +71,7 @@ div { text-align: justify; text-justify: inter-word; }
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('How do we solve these problems?', 2, None, '___sec22'),
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('A simple example', 2, None, '___sec23'),
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('Back to the more realistic cases', 2, None, '___sec24'),
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('Code Example', 2, None, '___sec25'),
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('Multiclass problems and regression with SVMs',
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2,
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None,
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'___sec26')]}
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('Code Example', 2, None, '___sec25')]}
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end of tocinfo -->
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<body>
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@@ -117,7 +113,7 @@ MathJax.Hub.Config({
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<center>[2] <b>Department of Physics and Astronomy and National Superconducting Cyclotron Laboratory, Michigan State University</b></center>
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<br>
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<p>
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<center><h4>Nov 10, 2019</h4></center> <!-- date -->
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<center><h4>Nov 15, 2019</h4></center> <!-- date -->
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<br>
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<p>
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<!-- !split --><br><br><br><br><br><br><br><br><br><br>
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@@ -1231,7 +1227,7 @@ sol[’primal objective’]
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<h2 id="___sec24">Back to the more realistic cases </h2>
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<p>
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We are now ready to return to our setup of the optmization problem for a more realistic case. Introducint the <b>slack</b> parameter \( C \) we have
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We are now ready to return to our setup of the optmization problem for a more realistic case. Introducing the <b>slack</b> parameter \( C \) we have
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$$
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\frac{1}{2} \boldsymbol{\lambda}^T\begin{bmatrix} y_1y_1K(\boldsymbol{x}_1,\boldsymbol{x}_1) & y_1y_2K(\boldsymbol{x}_1,\boldsymbol{x}_2) & \dots & \dots & y_1y_nK(\boldsymbol{x}_1,\boldsymbol{x}_n) \\
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y_2y_1K(\boldsymbol{x}_2,\boldsymbol{x}_1) & y_2y_2K(\boldsymbol{x}_2,\boldsymbol{x}_2) & \dots & \dots & y_1y_nK(\boldsymbol{x}_2,\boldsymbol{x}_n) \\
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@@ -1281,10 +1277,6 @@ res2 <span style="color: #666666">=</span> minimize(rosen, x0, method<span style
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<span style="color: #008000; font-weight: bold">print</span>(x0)
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</pre></div>
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<p>
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<!-- !split --><br><br><br><br><br><br><br><br><br><br>
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<h2 id="___sec26">Multiclass problems and regression with SVMs </h2>
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This material will be added later.
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<!-- ------------------- end of main content --------------- -->
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