Machine Learning: A Constraint-Based Approach - Softcover

Gori, Marco; Betti, Alessandro; Melacci, Stefano

Softcover

ISBN 10: 0323898599 ISBN 13: 9780323898591

Publisher: Morgan Kaufmann, 2023

Synopsis

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.

• Presents, in a unified manner, fundamental machine learning concepts, such as neural networks and kernel machines
• Provides in-depth coverage of unsupervised and semi-supervised learning, with new content in hot growth areas such as deep learning
• Includes a software simulator for kernel machines and learning from constraints that also covers exercises to facilitate learning
• Contains hundreds of solved examples and exercises chosen particularly for their progression of difficulty from simple to complex
• Supported by a free, downloadable companion book designed to facilitate students’ acquisition of experimental skills

About this title

About the Authors

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.

Alessandro Betti Ph.D. is a Postdoctoral Researcher in the Department of Information Engineering and Mathematics (DIISM) of the University of Siena (Siena, Italy). Dr. Betti’s interests include analysis of algorithms, discrete mathematics, tree structures, and formulation of “learning laws” through least action like principles.

Stefano Melacci Ph.D. is a Senior Researcher (Tenure-Track Assistant Professor) in the area of Computer Science at the Department of Information Engineering and Mathematics, University of Siena (Siena, Italy). He has been the Research Manager of the Italian company QuestIT S.r.l. (Siena, Italy) and a Research Fellow of the Department of Information Engineering and Mathematics, University of Siena, where he received his PhD (2010), and the M.S. Degree (cum Laude). Since 2017 he has served as Associate Editor for the IEEE Transactions on Neural Networks and Learning Systems, and he is an active reviewer for several journals and international conferences.

His profile is strongly characterized by research activity in the fields of Machine Learning and, more generally, Artificial Intelligence. Recently, he has been working on new technologies for Machine Learning-based Conversational Systems and he studied and proposed Multi-Layer architectures (Deep Networks) for extracting information from static images and videos, using adaptive convolutional filters and principles from Information Theory. He previously worked in the context of Kernel Machines and Regularization Theory, under the unifying framework of Learning from Constraints that allows classic learning models to integrate symbolic knowledge representations. He proposed Manifold Regularization-based algorithms and Neural Networks that implement Similarly Measures, with applications to Computer Vision.

From the Back Cover

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 includes 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. While regarding symbolic knowledge bases as a collection of constraints, the book draws a path towards a deep integration with machine learning that relies on the idea of adopting multivalued logic formalisms, like in fuzzy systems. Special attention is reserved 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, and includes 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.

This new edition is accompanied by a free downloadable companion book. The companion book focuses on providing concrete examples with in-depth discussions on coding and experiments. The reader is expected to use the companion book as a fast gateway to the discipline. At the same time, extensive referencing to the main textbook will stimulate and encourage the acquisition of foundational and mathematical details, along with algorithmic issues. The simple application-based problems covered in the book are solved by using multiple Python implementations of different Machine Learning models.

Edition details

• Publisher: Morgan Kaufmann
• Publication date: 2023
• Language: English
• ISBN 10: 0323898599
• ISBN 13: 9780323898591
• Binding: Paperback
• Edition number: 2
• Number of pages: 560