Machine Learning: A Constraint-Based Approach

by Marco Gori, Alessandro Betti, Stefano Melacci

Overview

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.

Product Details

ISBN-13:	9780323898591
Publisher:	Elsevier Science
Publication date:	05/23/2023
Edition description:	2nd ed.
Pages:	560
Product dimensions:	6.00(w) x 9.00(h) x (d)

About the Author

Professor Gori's research interests are in the field of artificial intelligence, with emphasis on machine learning and game playing. He is a co-author of the book “Web Dragons: Inside the myths of search engines technologies,” Morgan Kauffman (Elsevier), 2007. He was the Chairman of the Italian Chapter of the IEEE Computational Intelligence Society, and the President of the Italian Association for Artificial Intelligence. He is in the list of top Italian scientists kept by VIAAcademy

(http://www.topitalianscientists.org/top_italian_scientists.aspx). Dr. Gori is a fellow of the IEEE, ECCAI, and IAPR.

Table of Contents

1. The Big Picture2. Learning Principles3. Linear-Threshold Machines4. Kernel Machines5. Deep Architectures6. Learning and Reasoning with Constraints7. Epilogue8. Answers to selected exercises

Appendices:Constrained optimization in Finite DimensionsRegularization operatorsCalculus of variationsIndex to Notations

What People are Saying About This

From the Publisher

Provides a focused approach to our understanding of some of the deep ideas surrounding machine learning

Editorial Reviews

"The book is highly recommended for a machine learning course or self study from the statistical perspective that is based on constraint-based environments." —Zentralblatt MATH

From the Publisher