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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Semi-supervised scenario

A typical semi-supervised scenario is not very different from a supervised one. Let's suppose we have a data generating process, pdata:

However, contrary to a supervised approach, where we can rely on a completely labeled dataset, we have only a limited number N of data points drawn from pdata and provided with a label, as follows:

As for other methods, the training sample is assumed to be drawn uniformly, so as not to exclude any region of pdata. When this condition is met, it's possible to consider a larger amount (M) of unlabeled samples drawn from the marginal distribution :

The context of semi-supervised learning is then defined by the union of the two sets {XL, YL} and XU. An important assumption about the unlabeled samples is that their labels are supposed to be missing at random, without any correlation with the actual label distribution. The unlabeled dataset is assumed to have a distribution that doesn...