Neural Networks for Knowledge Representation and Inference
By Daniel S. Levine
"Neural Networks for Knowledge Representation and Inference" is published by Taylor & Francis Group in 2013 - Hoboken, it has 523 pages and the language of the book is English.
“Neural Networks for Knowledge Representation and Inference” Metadata:
- Title: ➤ Neural Networks for Knowledge Representation and Inference
- Author: Daniel S. Levine
- Language: English
- Number of Pages: 523
- Publisher: Taylor & Francis Group
- Publish Date: 2013
- Publish Location: Hoboken
“Neural Networks for Knowledge Representation and Inference” Subjects and Themes:
- Subjects: ➤ Neural networks (computer science) - Knowledge representation (information theory) - Réseaux neuronaux (Informatique) - Représentation des connaissances - COMPUTERS - Enterprise Applications - Business Intelligence Tools - Intelligence (AI) & Semantics - Théorie de la connaissance - Cognitive psychology - General - Social sciences -> psychology -> cognitive psychology - Scbe011515 - Scbe011518 - Social sciences -> psychology -> general
Edition Specifications:
- Pagination: 528
Edition Identifiers:
- The Open Library ID: OL34625134M - OL19363230W
- ISBN-13: 9781134771547 - 9780805811582
- All ISBNs: 9781134771547 - 9780805811582
AI-generated Review of “Neural Networks for Knowledge Representation and Inference”:
"Neural Networks for Knowledge Representation and Inference" Description:
Open Data:
The second published collection based on a conference sponsored by the Metroplex Institute for Neural Dynamics -- the first is Motivation, Emotion, and Goal Direction in Neural Networks (LEA, 1992) -- this book addresses the controversy between symbolicist artificial intelligence and neural network theory. A particular issue is how well neural networks -- well established for statistical pattern matching -- can perform the higher cognitive functions that are more often associated with symbolic approaches. This controversy has a long history, but recently erupted with arguments against t
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