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| LEADER |
00000cam a22000007i 4500 |
| 001 |
in00005771663 |
| 005 |
20260327174328.7 |
| 006 |
m o d |
| 007 |
cr ||||||||||| |
| 008 |
230202s2023 ne a ob 001 0 eng d |
| 040 |
|
|
|a UKMGB
|b eng
|e rda
|e pn
|c UKMGB
|d UKAHL
|d OPELS
|d OCLCF
|d OCLCO
|d OCLCL
|d YDX
|d OCLCL
|d SFB
|
| 015 |
|
|
|a GBC327275
|2 bnb
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| 016 |
7 |
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|a 020952121
|2 Uk
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| 019 |
|
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|a 1535407885
|
| 020 |
|
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|a 9780323984690
|q electronic publication
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| 020 |
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|a 032398469X
|q electronic book
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| 020 |
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|z 9780323898591
|q paperback
|
| 035 |
|
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|a (OCoLC)1376264568
|z (OCoLC)1535407885
|
| 037 |
|
|
|a 9780323984690
|b Ingram Content Group
|
| 050 |
|
4 |
|a Q325.5
|b .G67 2023
|
| 082 |
0 |
4 |
|a 006.31
|2 23
|
| 049 |
|
|
|a TXAM
|
| 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 |
|
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|a 1 online resource :
|b illustrations (black and white)
|
| 336 |
|
|
|a text
|b txt
|2 rdacontent
|
| 337 |
|
|
|a computer
|b c
|2 rdamedia
|
| 338 |
|
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|a online resource
|b cr
|2 rdacarrier
|
| 500 |
|
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|a Previous edition: published as by Marco Gori. 2018.
|
| 504 |
|
|
|a Includes bibliographical references and index.
|
| 500 |
|
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|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 |
|
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|a Description based upon online resource; title from PDF title page (viewed January 31st, 2025).
|
| 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.
|
| 505 |
0 |
|
|a 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.
|
| 650 |
|
0 |
|a Machine learning.
|
| 650 |
|
0 |
|a Algorithms.
|
| 650 |
|
6 |
|a Apprentissage automatique.
|
| 650 |
|
6 |
|a Algorithmes.
|
| 650 |
|
7 |
|a algorithms.
|2 aat
|
| 650 |
|
7 |
|a Algorithms
|2 fast
|
| 650 |
|
7 |
|a Machine learning
|2 fast
|
| 655 |
|
7 |
|a Electronic books.
|2 local
|
| 700 |
1 |
|
|a Betti, Alessandro,
|e author.
|
| 700 |
1 |
|
|a Melacci, Stefano,
|e author.
|
| 700 |
1 |
|
|a Gori, Marco.
|t Machine learning.
|
| 710 |
2 |
|
|a ScienceDirect (Online service)
|
| 758 |
|
|
|i has work:
|a Machine learning (Text)
|1 https://id.oclc.org/worldcat/entity/E39PCGMqwJkP4tmVh3B9KCXV3P
|4 https://id.oclc.org/worldcat/ontology/hasWork
|
| 776 |
0 |
8 |
|i Print version:
|z 9780323898591
|
| 776 |
0 |
8 |
|z 0-323-89859-9
|
| 856 |
4 |
0 |
|u http://proxy.library.tamu.edu/login?url=https://www.sciencedirect.com/science/book/9780323898591
|z Connect to the full text of this electronic book
|t 0
|
| 955 |
|
|
|a Elsevier ScienceDirect 2026-2027
|
| 994 |
|
|
|a 92
|b TXA
|
| 999 |
f |
f |
|i f63d447a-eadc-4ef4-9866-9ab835f6baa1
|s 2b30903a-0c03-41d7-9d52-2d78d2ee7b48
|t 0
|
| 952 |
f |
f |
|a Texas A&M University
|b College Station
|c Electronic Resources
|s www_evans
|d Available Online
|t 0
|e Q325.5 .G67 2023
|h Library of Congress classification
|
| 998 |
f |
f |
|a Q325.5 .G67 2023
|t 0
|l Available Online
|