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Morten Hjorth-Jensen
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About the course
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Review of Statistics with Resampling Techniques and Linear Algebra
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From Regression to Support Vector Machines
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Decision Trees, Ensemble Methods and Boosting
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Dimensionality Reduction
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Deep Learning Methods
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@@ -361,7 +367,72 @@ const thebe_selector_output = ".output, .cell_output"
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<h1>Support Vector Machines, overarching aims</h1>
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<h2> Contents </h2>
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8.1.1. The two-dimensional case
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8.1.2. A better approach
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8.2. A quick Reminder on Lagrangian Multipliers
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8.3. A soft classifier
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8.4. Kernels and non-linearity
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8.5. Different kernels and Mercer’s theorem
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8.6. The moons example
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8.7. Mathematical optimization of convex functions
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<div class="tex2jax_ignore mathjax_ignore section" id="support-vector-machines-overarching-aims">
@@ -1277,7 +1348,7 @@ converge. So, welcome to the promised land of quadratic programming.</p>
<div class="cell_output docutils container">
<div class="output traceback highlight-ipythontb notranslate"><div class="highlight"><pre><span></span><span class="gt">---------------------------------------------------------------------------</span>
<span class="ne">ModuleNotFoundError</span><span class="g g-Whitespace"> </span>Traceback (most recent call last)
<span class="o">&lt;</span><span class="n">ipython</span><span class="o">-</span><span class="nb">input</span><span class="o">-</span><span class="mi">4</span><span class="o">-</span><span class="mi">703716</span><span class="n">d317a7</span><span class="o">&gt;</span> <span class="ow">in</span> <span class="o">&lt;</span><span class="n">module</span><span class="o">&gt;</span>
<span class="nn">Input In [4],</span> in <span class="ni">&lt;cell line: 2&gt;</span><span class="nt">()</span>
<span class="g g-Whitespace"> </span><span class="mi">1</span> <span class="kn">import</span> <span class="nn">numpy</span>
<span class="ne">----&gt; </span><span class="mi">2</span> <span class="kn">import</span> <span class="nn">cvxopt</span>
@@ -1388,54 +1459,42 @@ With the slack constants this leads to the additional constraint <span class="m
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