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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...

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Detalles Bibliográficos
Clasificación:Libro Electrónico
Autores principales: Gori, Marco (Autor), Betti, Alessandro (Autor), Melacci, Stefano (Autor)
Formato: Electrónico eBook
Idioma:Inglés
Publicado: Amsterdam : Morgan Kaufmann, 2023.
Edición:Second edition /
Temas:
Acceso en línea:Texto completo

MARC

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100 1 |a Gori, Marco,  |e author.  |1 https://isni.org/isni/0000000116062239. 
245 1 0 |a Machine learning :  |b a constraint-based approach. 
250 |a Second edition /  |b Marco Gori, Alessandro Betti, Stefano Melacci. 
264 1 |a Amsterdam :  |b Morgan Kaufmann,  |c 2023. 
300 |a 1 online resource :  |b illustrations (black and white) 
336 |a text  |2 rdacontent 
336 |a still image  |2 rdacontent 
337 |a computer  |2 rdamedia 
338 |a online resource  |2 rdacarrier 
500 |a Previous edition: published as by Marco Gori. 2018. 
504 |a Includes bibliographical references and index. 
500 |a <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> 
588 |a Description based on CIP data; resource not viewed. 
520 |a 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. 
650 0 |a Machine learning. 
650 0 |a Algorithms. 
650 6 |a Apprentissage automatique.  |0 (CaQQLa)201-0131435 
650 6 |a Algorithmes.  |0 (CaQQLa)201-0001230 
650 7 |a algorithms.  |2 aat  |0 (CStmoGRI)aat300065585 
650 7 |a Algorithms  |2 fast  |0 (OCoLC)fst00805020 
650 7 |a Machine learning  |2 fast  |0 (OCoLC)fst01004795 
700 1 |a Betti, Alessandro,  |e author. 
700 1 |a Melacci, Stefano,  |e author. 
700 1 |a Gori, Marco.  |t Machine learning. 
776 0 8 |i Print version:  |z 9780323898591 
856 4 0 |u https://sciencedirect.uam.elogim.com/science/book/9780323898591  |z Texto completo