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

Transductive Support Vector Machines (TSVM)

Another approach to the same problem is offered by Transductive Support Vector Machines (TSVM), proposed by T. Joachims (in Joachims T., Transductive Inference for Text Classification using Support Vector Machines, ICML Vol. 99/1999). TSVM are particularly suited when the unlabeled sample isn't very noisy, and the overall structure of the dataset is trustworthy. A common application of TSVM is classification on a dataset containing data points drawn from the same data-generating process (for example, medical photos collected using the same instrument) but only partially labeled due to, for example, economic reasons. Since all the images can be trusted, TSVM can exploit the structure of the dataset to achieve an accuracy larger than the one reachable by a supervised classifier.