New Edition
Machine Learning,
Edition 1 A Constraint-Based Approach
By Marco Gori

Publication Date: 13 Nov 2017

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
About the author
By Marco Gori, Department of Information Engineering and Mathematics, University of Siena, Italy
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

Book details
ISBN: 9780081006597
Page Count: 580
Retail Price : £73.99
  • Theodoridis and Koutroumbas, Pattern Recognition, 4e, Academic Press, 9781597492720, Oct 2008, 984 pages, $109.00
  • Witten, Data Mining: Machine Learning Tools and Techniques, 4e, Morgan Kaufmann, 9780128042915, Jan 2011, 644 pages, $69.95
  • Han, Data Mining: Concepts and Techniques, 3e, Morgan Kaufmann, 9780123814791, Jun 2011, 744 pages, $74.95

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