Skip to main content

Save up to 20% on Elsevier print and eBooks with free shipping. No promo code needed.

Save up to 20% on print and eBooks.

Machine Learning

A Constraint-Based Approach

2nd Edition - March 1, 2023

Authors: Marco Gori, Alessandro Betti, Stefano Melacci

Language: English
Paperback ISBN:
9 7 8 - 0 - 3 2 3 - 8 9 8 5 9 - 1
eBook ISBN:
9 7 8 - 0 - 3 2 3 - 9 8 4 6 9 - 0

Machine Learning: A Constraint-Based Approach, Second Edition provides readers with a refreshing look at the basic models and algorithms of machine learning, with an emphas… Read more

Machine Learning

Purchase options

LIMITED OFFER

Save 50% on book bundles

Immediately download your ebook while waiting for your print delivery. No promo code is needed.

Institutional subscription on ScienceDirect

Request a sales quote
Machine Learning: A Constraint-Based Approach, Second Edition provides readers with a refreshing look at the basic models and algorithms of machine learning, with an emphasis on current topics of interest that include neural networks and kernel machines. The book presents the information in a truly unified manner that is based on the notion of learning from environmental constraints. It draws a path towards deep integration with machine learning that relies on the idea of adopting multivalued logic formalisms, such as in fuzzy systems. Special attention is given to deep learning, which nicely fits the constrained-based approach followed in this book.

The book presents a simpler unified notion of regularization, which is strictly connected with the parsimony principle, including many solved exercises that are classified according to the Donald Knuth ranking of difficulty, which essentially consists of a mix of warm-up exercises that lead to deeper research problems. A software simulator is also included.