Machine learning : a constraint-based approach / Marco Gori, Università di Siena.
By: Gori, Marco [author.]
Language: English Publisher: Cambridge, MA : Morgan Kaufmann Publishers, an imprint of Elsevier, [2018]Copyright date: ©2018Description: xx, 560 pages : illustrations ; 24 cmContent type: text Media type: unmediated Carrier type: volumeSubject(s): Machine learning | Algorithms | Algorithms | Machine learningDDC classification: 006.3/1 LOC classification: Q325.5 | .G67 2018| 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 2018 (Browse shelf) | Available | CITU-CL-48638 |
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| 006.31 B233 2012 Bayesian reasoning and machine learning / | 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 / |
About the Author
Marco Gori
Professor Gori's research interests are in the field of artificial intelligence, with emphasis on machine learning and game playing. He is a co-author of the book ?Web Dragons: Inside the myths of search engines technologies,? Morgan Kauffman (Elsevier), 2007. He was the Chairman of the Italian Chapter of the IEEE Computational Intelligence Society, and the President of the Italian Association for Artificial Intelligence. He is in the list of top Italian scientists kept by VIAAcademy
(http://www.topitalianscientists.org/top_italian_scientists.aspx). Dr. Gori is a fellow of the IEEE, ECCAI, and IAPR.
Includes bibliographical references (pages 534-551) and index.
Table of Contents
The Big Picture
Learning Principles
Linear-Threshold Machines
Kernel Machines
Deep Architectures
Learning and Reasoning with Constraints
Epilogue
Answers to selected exercises
Appendices:
Constrained optimization in Finite Dimensions
Regularization operators
Calculus of variations
Index to Notations
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. View more >
Key Features
Presents fundamental machine learning concepts, such as neural networks and kernel machines in a unified manner
Provides in-depth coverage of unsupervised and semi-supervised learning
Includes a software simulator for kernel machines and learning from constraints that also includes exercises to facilitate learning
Contains 250 solved examples and exercises chosen particularly for their progression of difficulty from simple to complex
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