Machine Learning: A ConstraintBased ApproachMorgan Kaufmann, 20 nov 2017  580 pagine Machine Learning: A ConstraintBased 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 warmup exercises that lead to deeper research problems. A software simulator is also included.

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 ConstraintBased Approach Marco Gori,Alessandro Betti,Stefano Melacci Anteprima limitata  2023 
Machine Learning: A ConstraintBased Approach Marco Gori,Alessandro Betti,Stefano Melacci Anteprima non disponibile  2023 