Machine Learning
A Constraint-Based Approach

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

Marco Gori (Author), Alessandro Betti (Author), Stefano Melacci (Author)

9780323898591, Elsevier Science

Paperback / softback, published 5 April 2023

560 pages
22.9 x 15.2 x 4.1 cm, 1.1 kg

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.

1. The Big Picture 2. Learning Principles 3. Linear-Threshold Machines 4. Kernel Machines 5. Deep Architectures 6. Learning from Constraints 7. Epilogue 8. Answers to selected exercises

Subject Areas: Computer vision [UYQV], Neural networks & fuzzy systems [UYQN], Machine learning [UYQM], Expert systems / knowledge-based systems [UYQE], Artificial intelligence [UYQ], Computer science [UY], Databases [UN], Algorithms & data structures [UMB], Discrete mathematics [PBD], Library, archive & information management [GLC]