Machine Learning: A Constraint-Based ApproachMorgan Kaufmann, 20 nov 2017 - 580 pagine 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.
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Sommario
60 | |
3 Linear Threshold Machines | 122 |
4 Kernel Machines | 186 |
5 Deep Architectures | 236 |
6 Learning and Reasoning With Constraints | 340 |
7 Epilogue | 446 |
8 Answers to Exercises | 452 |
C1 Functionals and Variations | 518 |
C2 Basic Notion on Variations | 520 |
C3 EulerLagrange Equations | 523 |
C4 Variational Problems With Subsidiary Conditions | 526 |
534 | |
552 | |
Back Cover | 561 |
Altre edizioni - Visualizza tutto
Machine Learning: A Constraint-Based Approach Marco Gori,Alessandro Betti,Stefano Melacci Anteprima limitata - 2023 |
Machine Learning: A Constraint-Based Approach Marco Gori,Alessandro Betti,Stefano Melacci Anteprima non disponibile - 2023 |