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Machine Learning: A Constraint-Based Approach
Marco Gori; Alessandro Betti; Stefano Melacci (Author)
·
Morgan Kaufmann
· Paperback
Machine Learning: A Constraint-Based Approach - Marco Gori; Alessandro Betti; Stefano Melacci
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Synopsis "Machine Learning: A Constraint-Based Approach"
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.
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All books in our catalog are Original.
The book is written in English.
The binding of this edition is Paperback.
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