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|a d'Avila Garcez, Artur S.
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| 245 |
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|a Neural-Symbolic Learning Systems
|h [electronic resource] :
|b Foundations and Applications /
|c by Artur S. d'Avila Garcez, Krysia B. Broda, Dov M. Gabbay.
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| 250 |
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|a 1st ed. 2002.
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| 264 |
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1 |
|a London :
|b Springer London :
|b Imprint: Springer,
|c 2002.
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| 300 |
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|a XIV, 271 p. 30 illus.
|b online resource.
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|a Perspectives in Neural Computing,
|x 1431-6854
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|a Springer eBook Collection
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| 505 |
0 |
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|a 1. Introduction and Overview -- 1.1 Why Integrate Neurons and Symbols? -- 1.2 Strategies of Neural-Symbolic Integration -- 1.3 Neural-Symbolic Learning Systems -- 1.4 A Simple Example -- 1.5 How to Read this Book -- 1.6 Summary -- 2. Background -- 2.1 General Preliminaries -- 2.2 Inductive Learning -- 2.3 Neural Networks -- 2.4 Logic Programming -- 2.5 Nonmonotonic Reasoning -- 2.6 Belief Revision -- I. Knowledge Refinement in Neural Networks -- 3. Theory Refinement in Neural Networks -- 4. Experiments on Theory Refinement -- II. Knowledge Extraction from Neural Networks -- 5. Knowledge Extraction from Trained Networks -- 6. Experiments on Knowledge Extraction -- III. Knowledge Revision in Neural Networks -- 7. Handling Inconsistencies in Neural Networks -- 8. Experiments on Handling Inconsistencies -- 9. Neural-Symbolic Integration: The Road Ahead.
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| 520 |
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|a Artificial Intelligence is concerned with producing devices that help or replace human beings in their daily activities. Neural-symbolic learning systems play a central role in this task by combining, and trying to benefit from, the advantages of both the neural and symbolic paradigms of artificial intelligence. This book provides a comprehensive introduction to the field of neural-symbolic learning systems, and an invaluable overview of the latest research issues in this area. It is divided into three sections, covering the main topics of neural-symbolic integration - theoretical advances in knowledge representation and learning, knowledge extraction from trained neural networks, and inconsistency handling in neural-symbolic systems. Each section provides a balance of theory and practice, giving the results of applications using real-world problems in areas such as DNA sequence analysis, power systems fault diagnosis, and software requirements specifications. Neural-Symbolic Learning Systems will be invaluable reading for researchers and graduate students in Engineering, Computing Science, Artificial Intelligence, Machine Learning and Neurocomputing. It will also be of interest to Intelligent Systems practitioners and anyone interested in applications of hybrid artificial intelligence systems.
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| 590 |
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|a Electronic access restricted to members of the Holy Cross Community.
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|a Artificial intelligence.
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|a Computers.
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| 650 |
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|a Electrical engineering.
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| 690 |
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|a Electronic resources (E-books)
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|a Broda, Krysia B.
|e author.
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| 700 |
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|a Gabbay, Dov M.
|e author.
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