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Machine Learning: A Constraint-Based Approach

Machine Learning: A Constraint-Based Approach

Machine Learning: A Constraint-Based Approach
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Machine Learning: A Constraint-Based Approach Paperback - 2017

by Gori Ph.D., Marco

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paperback. Good. Access codes and supplements are not guaranteed with used items. May be an ex-library book.
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Reader reviews for Machine Learning: A Constraint-Based Approach

From the publisher

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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