| Edition |
Second edition / Marco Gori, Alessandro Betti, Stefano Melacci. |
| Description |
1 online resource : illustrations (black and white) |
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text rdacontent |
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still image rdacontent |
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computer rdamedia |
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online resource rdacarrier |
| Note |
Previous edition: published as by Marco Gori. 2018. |
| Bibliography |
Includes bibliographical references and index. |
| Note |
<p>1. The Big Picture 2. Learning Principles 3. Linear-Threshold Machines 4. Kernel Machines 5. Deep Architectures 6. Learning from Constraints 7. Epilogue 8. Answers to selected exercises</p> |
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Description based on CIP data; resource not viewed. |
| Summary |
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. |
| Subject |
Machine learning.
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Algorithms.
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Apprentissage automatique.
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Algorithmes.
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algorithms.
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Algorithms
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Machine learning
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| Added Author |
Betti, Alessandro, author.
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Melacci, Stefano, author.
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Gori, Marco.
Machine learning.
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| Other Form: |
Print version: 9780323898591 |
| ISBN |
9780323984690 ePub ebook |
|
032398469X |
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9780323898591 paperback |
| Standard No. |
UKMGB 020952121 |
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AU@ 000073813814 |
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