Book Image

Mastering Machine Learning Algorithms. - Second Edition

By : Giuseppe Bonaccorso
Book Image

Mastering Machine Learning Algorithms. - Second Edition

By: Giuseppe Bonaccorso

Overview of this book

Mastering Machine Learning Algorithms, Second Edition helps you harness the real power of machine learning algorithms in order to implement smarter ways of meeting today's overwhelming data needs. This newly updated and revised guide will help you master algorithms used widely in semi-supervised learning, reinforcement learning, supervised learning, and unsupervised learning domains. You will use all the modern libraries from the Python ecosystem – including NumPy and Keras – to extract features from varied complexities of data. Ranging from Bayesian models to the Markov chain Monte Carlo algorithm to Hidden Markov models, this machine learning book teaches you how to extract features from your dataset, perform complex dimensionality reduction, and train supervised and semi-supervised models by making use of Python-based libraries such as scikit-learn. You will also discover practical applications for complex techniques such as maximum likelihood estimation, Hebbian learning, and ensemble learning, and how to use TensorFlow 2.x to train effective deep neural networks. By the end of this book, you will be ready to implement and solve end-to-end machine learning problems and use case scenarios.
Table of Contents (28 chapters)
26
Other Books You May Enjoy
27
Index

Contrastive Pessimistic Likelihood Estimation

As we discussed in the previous chapter, in many real-life problems, it's cheaper to retrieve unlabeled samples, rather than correctly labeled ones. For this reason, many researchers have worked to find out the best strategies to carry out a semi-supervised classification that could outperform its supervised counterpart. The idea is to train a classifier with a few labeled samples and then improve its accuracy after adding weighted unlabeled samples. One of the best results is the CPLE algorithm, proposed by Loog (in Loog M., Contrastive Pessimistic Likelihood Estimation for Semi-Supervised Classification, arXiv:1503.00269, 2015).

Before we can explain this algorithm, it's necessary to define Platt scaling. If we have a labeled dataset (X, Y) containing N samples, it's possible to define the log-likelihood cost function of a generic estimator, as follows:

After training the model, it should be possible to determine...