Machine learning : a constraint-based approach / Marco Gori, Alessandro Betti, Stefano Melacci.
By: Gori, Marco [author.]
Contributor(s): Betti, Alessandro [author.] | Melacci, Stefano [author.]
Language: English Publisher: Cambridge, MA MK, Morgan Kaufmann Publishers, an imprint of Elsevier [2024]Copyright date: ©2024Edition: Second editionDescription: xviii, 537 pages : illustrations ; 24 cmContent type: text Media type: unmediated Carrier type: volumeISBN: 9780323898591; 0323898599Subject(s): Machine learning | AlgorithmsDDC classification: 006.3/1 LOC classification: Q325.5 | .G67 2024Summary: 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.| Item type | Current location | Home library | Call number | Status | Date due | Barcode | Item holds |
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COLLEGE LIBRARY | COLLEGE LIBRARY SUBJECT REFERENCE | 006.31 G675 2024 (Browse shelf) | Available (In Process) |
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| 006.31 B413 2015 Machine learning : hands-on for developers and technical professionals / | 006.31 C839 2005 Fuzzy modeling and genetic algorithms for data mining and exploration / | 006.31 G675 2018 Machine learning : a constraint-based approach / | 006.31 G675 2024 Machine learning : a constraint-based approach / | 006.31 In8 2007 Introduction to statistical relational learning / | 006.31 M131 1986 Machine learning : an artificial intelligence approach / | 006.31 M359 2009 Machine learning : an algorithmic perspective / |
Previous edition: published as by Marco Gori. 2018.
Includes bibliographical references and index.
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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