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
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27
Index

Co-Training

Co-Training is another very simple but effective semi-supervised approach, proposed by Blum and Mitchell (in Blum A., Mitchell T., Combining Labeled and Unlabeled Data with Co-Training, 11th Annual Conference on Computational Learning Theory, 1998) as an alternative strategy when the dataset is a multidimensional one, and different groups of features encode different but still peculiar aspects of each class. Co-Training is effective only in scenarios where the data points can be theoretically classified using only a part of the features (even if with a light performance loss). As we're going to see, the redundancy becomes helpful in presence of an unlabeled sample, to compensate for the lack of knowledge that a single classifier might have. On the contrary, if every data point contains features that cannot be split into two separate and autonomous groups, this method is ineffective.

Co-Training theory

Let's suppose we have a labeled dataset {XL, YL} with...