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)
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The algorithms discussed in this chapter are generally more powerful than those analyzed in the previous one, but they have specific differences that must always be considered. CPLE and S3VM are inductive methods.

CPLE is an inductive, semi-supervised classification framework based on statistical learning concepts that can be adopted together with any supervised classifier. The main concept is to define a contrastive log-likelihood based on soft-labels that takes into account both labeled and unlabeled samples. The importance granted to the latter is conditioned to the maximization of the log-likelihood, and therefore the algorithm is less suited to tasks where fine control is needed.

Another inductive classification approach is provided by the S3VM algorithm, which is an extension of the classical SVM approach, based on two extra optimization constraints to address the unlabeled samples. This method is relatively powerful, but it's non-convex and, therefore, very...