
Machine Learning
A Constraint-Based Approach
Marco Gori(Author)
Morgan Kaufmann (Publisher)
Published on 13. November 2017
Book
Paperback/Softback
580 pages
978-0-08-100659-7 (ISBN)
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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.
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.
Reviews / Votes
"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
More details
Language
English
Place of publication
London
United Kingdom
Publishing group
Elsevier Science & Technology
Target group
College/higher education
Upper level undergraduate and graduate students taking a machine learning course in computer science departments and professionals involved in relevant areas of artificial intelligence
Dimensions
Height: 235 mm
Width: 191 mm
Weight
810 gr
ISBN-13
978-0-08-100659-7 (9780081006597)
Copyright in bibliographic data and cover images is held by Nielsen Book Services Limited or by the publishers or by their respective licensors: all rights reserved.
Schweitzer Classification
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04/2023
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Morgan Kaufmann
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E-Book
11/2017
1st Edition
Morgan Kaufmann
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Person
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.
Content
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
Appendices:Constrained optimization in Finite DimensionsRegularization operatorsCalculus of variationsIndex to Notations