Morten Hjorth-Jensen
447 lines
28 KiB
HTML
447 lines
28 KiB
HTML
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<a class="navbar-brand" href="week47-bs.html">Week 47: From Decision Trees to Ensemble Methods, Random Forests and Boosting Methods and Summary of Course</a>
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<a href="#" class="dropdown-toggle" data-toggle="dropdown">Contents <b class="caret"></b></a>
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<ul class="dropdown-menu">
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<!-- navigation toc: --> <li><a href="._week47-bs001.html#plan-for-week-47" style="font-size: 80%;">Plan for week 47</a></li>
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<!-- navigation toc: --> <li><a href="._week47-bs002.html#bagging" style="font-size: 80%;">Bagging</a></li>
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<!-- navigation toc: --> <li><a href="._week47-bs003.html#more-bagging" style="font-size: 80%;">More bagging</a></li>
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<!-- navigation toc: --> <li><a href="._week47-bs004.html#making-your-own-bootstrap-changing-the-level-of-the-decision-tree" style="font-size: 80%;">Making your own Bootstrap: Changing the Level of the Decision Tree</a></li>
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<!-- navigation toc: --> <li><a href="._week47-bs005.html#random-forests" style="font-size: 80%;">Random forests</a></li>
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<!-- navigation toc: --> <li><a href="._week47-bs006.html#random-forest-algorithm" style="font-size: 80%;">Random Forest Algorithm</a></li>
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<!-- navigation toc: --> <li><a href="._week47-bs007.html#random-forests-compared-with-other-methods-on-the-cancer-data" style="font-size: 80%;">Random Forests Compared with other Methods on the Cancer Data</a></li>
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<!-- navigation toc: --> <li><a href="._week47-bs008.html#compare-bagging-on-trees-with-random-forests" style="font-size: 80%;">Compare Bagging on Trees with Random Forests</a></li>
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<!-- navigation toc: --> <li><a href="._week47-bs009.html#boosting-a-bird-s-eye-view" style="font-size: 80%;">Boosting, a Bird's Eye View</a></li>
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<!-- navigation toc: --> <li><a href="._week47-bs010.html#what-is-boosting-additive-modelling-iterative-fitting" style="font-size: 80%;">What is boosting? Additive Modelling/Iterative Fitting</a></li>
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<!-- navigation toc: --> <li><a href="._week47-bs011.html#iterative-fitting-regression-and-squared-error-cost-function" style="font-size: 80%;">Iterative Fitting, Regression and Squared-error Cost Function</a></li>
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<!-- navigation toc: --> <li><a href="._week47-bs012.html#squared-error-example-and-iterative-fitting" style="font-size: 80%;">Squared-Error Example and Iterative Fitting</a></li>
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<!-- navigation toc: --> <li><a href="._week47-bs013.html#iterative-fitting-classification-and-adaboost" style="font-size: 80%;">Iterative Fitting, Classification and AdaBoost</a></li>
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<!-- navigation toc: --> <li><a href="._week47-bs014.html#adaptive-boosting-adaboost" style="font-size: 80%;">Adaptive Boosting, AdaBoost</a></li>
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<!-- navigation toc: --> <li><a href="._week47-bs015.html#building-up-adaboost" style="font-size: 80%;">Building up AdaBoost</a></li>
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<!-- navigation toc: --> <li><a href="._week47-bs016.html#adaptive-boosting-adaboost-basic-algorithm" style="font-size: 80%;">Adaptive boosting: AdaBoost, Basic Algorithm</a></li>
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<!-- navigation toc: --> <li><a href="._week47-bs017.html#basic-steps-of-adaboost" style="font-size: 80%;">Basic Steps of AdaBoost</a></li>
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<!-- navigation toc: --> <li><a href="._week47-bs018.html#adaboost-examples" style="font-size: 80%;">AdaBoost Examples</a></li>
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<!-- navigation toc: --> <li><a href="._week47-bs019.html#gradient-boosting-basics-with-steepest-descent-functional-gradient-descent" style="font-size: 80%;">Gradient boosting: Basics with Steepest Descent/Functional Gradient Descent</a></li>
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<!-- navigation toc: --> <li><a href="._week47-bs020.html#the-squared-error-again-steepest-descent" style="font-size: 80%;">The Squared-Error again! Steepest Descent</a></li>
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<!-- navigation toc: --> <li><a href="._week47-bs021.html#steepest-descent-example" style="font-size: 80%;">Steepest Descent Example</a></li>
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<!-- navigation toc: --> <li><a href="._week47-bs022.html#gradient-boosting-algorithm" style="font-size: 80%;">Gradient Boosting, algorithm</a></li>
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<!-- navigation toc: --> <li><a href="._week47-bs023.html#gradient-boosting-examples-of-regression" style="font-size: 80%;">Gradient Boosting, Examples of Regression</a></li>
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<!-- navigation toc: --> <li><a href="._week47-bs024.html#gradient-boosting-classification-example" style="font-size: 80%;">Gradient Boosting, Classification Example</a></li>
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<!-- navigation toc: --> <li><a href="._week47-bs025.html#xgboost-extreme-gradient-boosting" style="font-size: 80%;">XGBoost: Extreme Gradient Boosting</a></li>
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<!-- navigation toc: --> <li><a href="._week47-bs026.html#regression-case" style="font-size: 80%;">Regression Case</a></li>
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<!-- navigation toc: --> <li><a href="._week47-bs027.html#xgboost-on-the-cancer-data" style="font-size: 80%;">Xgboost on the Cancer Data</a></li>
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<!-- navigation toc: --> <li><a href="._week47-bs028.html#summary-of-course" style="font-size: 80%;">Summary of course</a></li>
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<!-- navigation toc: --> <li><a href="._week47-bs029.html#what-me-worry-no-final-exam-in-this-course" style="font-size: 80%;">What? Me worry? No final exam in this course!</a></li>
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<!-- navigation toc: --> <li><a href="._week47-bs030.html#what-is-the-link-between-artificial-intelligence-and-machine-learning-and-some-general-remarks" style="font-size: 80%;">What is the link between Artificial Intelligence and Machine Learning and some general Remarks</a></li>
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<!-- navigation toc: --> <li><a href="._week47-bs031.html#going-back-to-the-beginning-of-the-semester" style="font-size: 80%;">Going back to the beginning of the semester</a></li>
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<!-- navigation toc: --> <li><a href="._week47-bs032.html#not-so-sharp-distinctions" style="font-size: 80%;">Not so sharp distinctions</a></li>
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<!-- navigation toc: --> <li><a href="._week47-bs033.html#topics-we-have-covered-this-year" style="font-size: 80%;">Topics we have covered this year</a></li>
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<!-- navigation toc: --> <li><a href="._week47-bs034.html#statistical-analysis-and-optimization-of-data" style="font-size: 80%;">Statistical analysis and optimization of data</a></li>
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<!-- navigation toc: --> <li><a href="._week47-bs035.html#machine-learning" style="font-size: 80%;">Machine learning</a></li>
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<!-- navigation toc: --> <li><a href="._week47-bs036.html#learning-outcomes-and-overarching-aims-of-this-course" style="font-size: 80%;">Learning outcomes and overarching aims of this course</a></li>
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<!-- navigation toc: --> <li><a href="._week47-bs037.html#perspective-on-machine-learning" style="font-size: 80%;">Perspective on Machine Learning</a></li>
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<!-- navigation toc: --> <li><a href="._week47-bs038.html#machine-learning-research" style="font-size: 80%;">Machine Learning Research</a></li>
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<!-- navigation toc: --> <li><a href="._week47-bs039.html#starting-your-machine-learning-project" style="font-size: 80%;">Starting your Machine Learning Project</a></li>
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<!-- navigation toc: --> <li><a href="._week47-bs040.html#choose-a-model-and-algorithm" style="font-size: 80%;">Choose a Model and Algorithm</a></li>
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<!-- navigation toc: --> <li><a href="._week47-bs041.html#preparing-your-data" style="font-size: 80%;">Preparing Your Data</a></li>
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<!-- navigation toc: --> <li><a href="._week47-bs042.html#which-activation-and-weights-to-choose-in-neural-networks" style="font-size: 80%;">Which Activation and Weights to Choose in Neural Networks</a></li>
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<!-- navigation toc: --> <li><a href="._week47-bs043.html#optimization-methods-and-hyperparameters" style="font-size: 80%;">Optimization Methods and Hyperparameters</a></li>
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<!-- navigation toc: --> <li><a href="._week47-bs044.html#resampling" style="font-size: 80%;">Resampling</a></li>
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|
<!-- navigation toc: --> <li><a href="._week47-bs045.html#other-courses-on-data-science-and-machine-learning-at-uio" style="font-size: 80%;">Other courses on Data science and Machine Learning at UiO</a></li>
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<!-- navigation toc: --> <li><a href="._week47-bs046.html#additional-courses-of-interest" style="font-size: 80%;">Additional courses of interest</a></li>
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<!-- navigation toc: --> <li><a href="._week47-bs047.html#what-s-the-future-like" style="font-size: 80%;">What's the future like?</a></li>
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<!-- navigation toc: --> <li><a href="._week47-bs048.html#types-of-machine-learning-a-repetition" style="font-size: 80%;">Types of Machine Learning, a repetition</a></li>
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<!-- navigation toc: --> <li><a href="._week47-bs049.html#why-boltzmann-machines" style="font-size: 80%;">Why Boltzmann machines?</a></li>
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<a name="part0065"></a>
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<h2 id="adversarial-learning" class="anchor">Adversarial learning </h2>
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<p>The conventional deep generative model has a potential problem: the
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model tends to generate extreme instances to maximize the
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probabilistic likelihood, which will hurt its performance. Adversarial
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learning utilizes the adversarial behaviors (e.g., generating
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adversarial instances or training an adversarial model) to enhance the
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robustness of the model and improve the quality of the generated
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data. In recent years, one of the most promising unsupervised learning
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technologies, generative adversarial networks (GAN), has already been
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successfully applied to image, speech, and text.
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</p>
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<p><a href="https://www.youtube.com/watch?v=CIfsB_EYsVI&ab_channel=StanfordUniversitySchoolofEngineering" target="_self">Lecture on adversial learning</a>.</p>
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