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Machine Learning

A Constraint-Based Approach

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

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Description

Machine Learning: A Constraint-Based Approach 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. A special attention is reserved to deep learning, which nicely fits the constrained- based approach followed in this book.

This 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.

Key features

  • Presents fundamental machine learning concepts, such as neural networks and kernel machines in a unified manner
  • Provides in-depth coverage of unsupervised and semi-supervised learning
  • Includes a software simulator for kernel machines and learning from constraints that also includes exercises to facilitate learning
  • Contains 250 solved examples and exercises chosen particularly for their progression of difficulty from simple to complex

Readership

Upper level undergraduate and graduate students taking a machine learning course in computer science departments and professionals involved in relevant areas of artificial intelligence

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

Review quotes

"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

"The book introduces machine learning from the statistical perspective introducing constraint-based environments by combining symbolic constraints and sub-symbolic representations…. The book is highly recommended for a machine learning course or self study from the statistical perspective that is based on constraint-based environments."—Andreas Wichert, zbMATHOpen

Product details

About the author

MG

Marco Gori

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.

Affiliations and expertise
Department of Information Engineering and Mathematics, University of Siena, Italy

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