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

Summary

In this chapter, we have introduced the most important label propagation techniques. In particular, we have seen how to build a dataset graph based on a weighting kernel, and how to use the geometric information provided by unlabeled samples to determine the most likely class. The basic approach works by iterating the multiplication of the label vector times the weight matrix until a stable point is reached and we have proven that, under simple assumptions, it is always possible.

Another approach, implemented by scikit-learn, is based on the transition probability from a state (represented by a sample) to another one, until the convergence to a labeled point. The probability matrix is obtained using a normalized weight matrix to encourage transitions associated with close points and discourage all the long jumps. The main drawback of these two methods is the hard clamping of labeled samples; this constraint can be useful if we trust our dataset, but it can be a limitation...