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 Support Vector Machines (S³VM)

When we discussed the cluster assumption in the previous chapter, we also defined the low-density regions as boundaries and the corresponding problem as low-density separation. A common supervised classifier based on this concept is a Support Vector Machine (SVM), the objective of which is to maximize the distance between the dense regions where the samples must be.

S³VM Theory

For a complete description of linear and kernel-based SVMs, please refer to Bonaccorso G., Machine Learning Algorithms, Second Edition, Packt Publishing, 2018. However, it's useful to remind yourself of the basic model for a linear SVM with slack variables :

This model is based on the assumption that y_i can be either -1 or 1. The slack variables or soft-margins are variables, one for each sample, introduced to reduce the strength imposed by the original condition (min ||w||), which is based on a hard margin that misclassifies...