
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.
Product Details
Publisher | Morgan Kaufmann Publishers |
Publish Date | November 13, 2017 |
Pages | 580 |
Language | English |
Type | |
EAN/UPC | 9780081006597 |
Dimensions | 9.1 X 7.5 X 1.0 inches | 2.6 pounds |
About the Author
(http: //www.topitalianscientists.org/top_italian_scientists.aspx). Dr. Gori is a fellow of the IEEE, ECCAI, and IAPR.
Reviews
"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
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