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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Index

Transductive Support Vector Machines (TSVM)

Another approach to the same problem is offered by Transductive Support Vector Machines (TSVM), proposed by T. Joachims (in Joachims T., Transductive Inference for Text Classification using Support Vector Machines, ICML Vol. 99/1999). TSVM are particularly suited when the unlabeled sample isn't very noisy, and the overall structure of the dataset is trustworthy. A common application of TSVM is classification on a dataset containing data points drawn from the same data-generating process (for example, medical photos collected using the same instrument) but only partially labeled due to, for example, economic reasons. Since all the images can be trusted, TSVM can exploit the structure of the dataset to achieve an accuracy larger than the one reachable by a supervised classifier.

TSVM Theory

The idea is to keep the original objective with two sets of slack variables – the first for the labeled samples and the other...