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Mastering Machine Learning Algorithms

You're reading from   Mastering Machine Learning Algorithms Expert techniques to implement popular machine learning algorithms and fine-tune your models

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Product type Paperback
Published in May 2018
Publisher Packt
ISBN-13 9781788621113
Length 576 pages
Edition 1st Edition
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Author (1):
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Giuseppe Bonaccorso Giuseppe Bonaccorso
Author Profile Icon Giuseppe Bonaccorso
Giuseppe Bonaccorso
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Table of Contents (17) Chapters Close

Preface 1. Machine Learning Model Fundamentals FREE CHAPTER 2. Introduction to Semi-Supervised Learning 3. Graph-Based Semi-Supervised Learning 4. Bayesian Networks and Hidden Markov Models 5. EM Algorithm and Applications 6. Hebbian Learning and Self-Organizing Maps 7. Clustering Algorithms 8. Ensemble Learning 9. Neural Networks for Machine Learning 10. Advanced Neural Models 11. Autoencoders 12. Generative Adversarial Networks 13. Deep Belief Networks 14. Introduction to Reinforcement Learning 15. Advanced Policy Estimation Algorithms 16. Other Books You May Enjoy

AdaBoost


In the previous section, we have seen that sampling with a replacement leads to datasets where the samples are randomly reweighted. However, if M is very large, most of the samples will appear only once and, moreover, all the choices are totally random. AdaBoost is an algorithm proposed by Schapire and Freund that tries to maximize the efficiency of each weak learner by employing adaptive boosting (the name derives from this). In particular, the ensemble is grown sequentially and the data distribution is recomputed at each step so as to increase the weight of those samples that were misclassified and reduce the weight of the ones that were correctly classified. In this way, every new learner is forced to focus on those regions that were more problematic for the previous estimators. The reader can immediately understand that, contrary to random forests and other bagging methods, boosting doesn't rely on randomness to reduce the variance and improve the accuracy. Rather, it works...

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