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

Semi-supervised scenario

A typical semi-supervised scenario is not very different from a supervised one. Let's suppose we have a data generating process, p_data:

However, contrary to a supervised approach, where we can rely on a completely labeled dataset, we have only a limited number N of data points drawn from p_data and provided with a label, as follows:

As for other methods, the training sample is assumed to be drawn uniformly, so as not to exclude any region of p_data. When this condition is met, it's possible to consider a larger amount (M) of unlabeled samples drawn from the marginal distribution :

The context of semi-supervised learning is then defined by the union of the two sets {X_L, Y_L} and X_U. An important assumption about the unlabeled samples is that their labels are supposed to be missing at random, without any correlation with the actual label distribution. The unlabeled dataset is assumed to have a distribution that doesn...