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<h1>Note for "Thesis - Behavior of Machine Learning Algorithms in Adversarial Environments.pdf"(1)</h1>
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<h2 id="toc_0">1.1 Motivation and Methodology</h2>
<h4 id="toc_1">Learning approach is well-suited to the scenario when:</h4>
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<li>The process is too complex to designed for human operator</li>
<li>Requirement of dynamical development</li>
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<span class="date">2016/2/4 16:55 下午</span>
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<h1>Notes for "ICMLC2009-FabioRoli.pdf"</h1>
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<h2 id="toc_0">Understanding:</h2>
<h3 id="toc_1">1. What is adversarial classification? Basic concepts and motivations</h3>
<p>The Classifier which take the adversary actions into account. It can develop according to the adversary actions.</p>
<p>Its motivations is that the classical model cannot perform well in adversarial environments. Because the classical model is build and set up base on the random noise, it’s also use for normal random noise environment. But in adversarial environment, the noise it face is adversarial noise, which is generated by adversary on purpose.</p>
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<span class="date">2016/1/29 14:14 下午</span>
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