Machine Learning: A Constraint-Based Approach
Language: English
Published by Morgan Kaufmann, 2023
- Softcover
- New
Seller: Books Puddle, Woodside, NY, U.S.A.Books Puddle
AbeBooks seller since November 22, 2018
Condition: New
£ 76.58
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2nd edition NO-PA16APR2015-KAP.
Seller Inventory # 26396064792
- Title
- Machine Learning: A Constraint-Based Approach
- Author
- Gori, Marco; Betti, Alessandro; Melacci, Stefano
- Publisher
- Morgan Kaufmann
- Publication year
- 2023
- Condition
- New
- Binding
- Soft cover
- Language
- English
- ISBN 10
- 0323898599
- ISBN 13
- 9780323898591
- Edition
- 2nd Edition
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.
- Presents, in a unified manner, fundamental machine learning concepts, such as neural networks and kernel machines
- Provides in-depth coverage of unsupervised and semi-supervised learning, with new content in hot growth areas such as deep learning
- Includes a software simulator for kernel machines and learning from constraints that also covers exercises to facilitate learning
- Contains hundreds of solved examples and exercises chosen particularly for their progression of difficulty from simple to complex
- Supported by a free, downloadable companion book designed to facilitate students’ acquisition of experimental skills
"Synopsis" may belong to another edition of this title.
About the Author
(http://www.topitalianscientists.org/top_italian_scientists.aspx). Dr. Gori is a fellow of the IEEE, ECCAI, and IAPR.
Alessandro Betti Ph.D. is a Postdoctoral Researcher in the Department of Information Engineering and Mathematics (DIISM) of the University of Siena (Siena, Italy). Dr. Betti’s interests include analysis of algorithms, discrete mathematics, tree structures, and formulation of “learning laws through least action like principles.
Stefano Melacci Ph.D. is a Senior Researcher (Tenure-Track Assistant Professor) in the area of Computer Science at the Department of Information Engineering and Mathematics, University of Siena (Siena, Italy). He has been the Research Manager of the Italian company QuestIT S.r.l. (Siena, Italy) and a Research Fellow of the Department of Information Engineering and Mathematics, University of Siena, where he received his PhD (2010), and the M.S. Degree (cum Laude). Since 2017 he has served as Associate Editor for the IEEE Transactions on Neural Networks and Learning Systems, and he is an active reviewer for several journals and international conferences.
His profile is strongly characterized by research activity in the fields of Machine Learning and, more generally, Artificial Intelligence. Recently, he has been working on new technologies for Machine Learning-based Conversational Systems and he studied and proposed Multi-Layer architectures (Deep Networks) for extracting information from static images and videos, using adaptive convolutional filters and principles from Information Theory. He previously worked in the context of Kernel Machines and Regularization Theory, under the unifying framework of Learning from Constraints that allows classic learning models to integrate symbolic knowledge representations. He proposed Manifold Regularization-based algorithms and Neural Networks that implement Similarly Measures, with applications to Computer Vision.
"About the title" may belong to another edition of this title.
Books Puddle
Woodside, NY, U.S.A.
AbeBooks seller since November 22, 2018
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