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

View More author details

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

Fuzzy C-means

We have already talked about the difference between hard and soft clustering, comparing K-means with Gaussian mixtures. Another way to address this problem is based on the concept of fuzzy logic, which was proposed for the first time by Lotfi Zadeh in 1965 (for further details, a very good reference is Pedrycz W., Gomide F., An Introduction to Fuzzy Sets, The MIT Press, 1998). Classic logic sets are based on the law of excluded middle, which in a clustering scenario can be expressed by saying that a point can belong only to a single cluster c_j.

Speaking more generally, if we split our universe into labeled partitions, a hard clustering approach would assign a label to each sample, while a fuzzy (or soft) approach would allow the management of a membership degree (in Gaussian mixtures, this is an actual probability) w_ij, which expresses how strong the relationship is between point and cluster c_j.