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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Label propagation

Label propagation is a family of semi-supervised algorithms that rely on the graph representation of a dataset to exploit the existing relations among nodes in order to propagate the labels to unlabeled points. In particular, if we have N labeled points (with bipolar labels +1 and –1) and M unlabeled points (denoted by y = 0), it's possible to build an undirected graph based on a measure of geometric affinity among samples. In the following figure, there's an example of such a structure:

Example of binary graph

The graph is defined as a structure containing two sets G = {V, E}. E is the set of vertices (or nodes) and contains all sample labels V = {–1, +1, 0}, while the edge set E is based on an affinity measure that encodes the strength of the relation between two nodes. For practical reasons, it's helpful to introduce a matrix W whose elements wij are:

  • The actual weight of the edge (i, j). In this case...