You're reading from Mastering Machine Learning Algorithms Expert techniques for implementing popular machine learning algorithms, fine-tuning your models, and understanding how they work

Product type Paperback

Published in Jan 2020

Publisher Packt

ISBN-13 9781838820299

Length 798 pages

Edition 2nd Edition

Languages

Python

Tools

Keras

Concepts

Machine Learning

Authors (2):

Giuseppe Bonaccorso

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Table of Contents (28) Chapters

Preface

1. Machine Learning Model Fundamentals

2. Loss Functions and Regularization FREE CHAPTER

3. Introduction to Semi-Supervised Learning

4. Advanced Semi-Supervised Classification

5. Graph-Based Semi-Supervised Learning

6. Clustering and Unsupervised Models

7. Advanced Clustering and Unsupervised Models

8. Clustering and Unsupervised Models for Marketing

9. Generalized Linear Models and Regression

10. Introduction to Time-Series Analysis

11. Bayesian Networks and Hidden Markov Models

12. The EM Algorithm

13. Component Analysis and Dimensionality Reduction

14. Hebbian Learning

15. Fundamentals of Ensemble Learning

16. Advanced Boosting Algorithms

17. Modeling Neural Networks

18. Optimizing Neural Networks

19. Deep Convolutional Networks

20. Recurrent Neural Networks

21. Autoencoders

22. Introduction to Generative Adversarial Networks

23. Deep Belief Networks

24. Introduction to Reinforcement Learning

25. Advanced Policy Estimation Algorithms

26. Other Books You May Enjoy

27. Index

Co-Training

Co-Training is another very simple but effective semi-supervised approach, proposed by Blum and Mitchell (in Blum A., Mitchell T., Combining Labeled and Unlabeled Data with Co-Training, 11th Annual Conference on Computational Learning Theory, 1998) as an alternative strategy when the dataset is a multidimensional one, and different groups of features encode different but still peculiar aspects of each class. Co-Training is effective only in scenarios where the data points can be theoretically classified using only a part of the features (even if with a light performance loss). As we're going to see, the redundancy becomes helpful in presence of an unlabeled sample, to compensate for the lack of knowledge that a single classifier might have. On the contrary, if every data point contains features that cannot be split into two separate and autonomous groups, this method is ineffective.