DocumentCode :
1947510
Title :
Categorical Mapping from Ontology to Neural Network: Initial Studies of Simple Neural Networks´ Concept Capacity
Author :
Taylor, Shawn E. ; Healy, Michael J. ; Caudell, Thomas P.
Author_Institution :
Univ. of New Mexico, Albuquerque
fYear :
2007
fDate :
12-17 Aug. 2007
Firstpage :
2020
Lastpage :
2025
Abstract :
A recent neural network semantic theory provides the framework for mapping ontologies to neural networks. We use category theory, the mathematical theory of structure, to explore the concept representational abilities of select neural networks. Methodologies suggested by the semantic theory have been gainfully applied to specific applications. This paper describes a rigorous and numerical study of the implementation of neural category representations into an actual neural network.
Keywords :
neural nets; ontologies (artificial intelligence); categorical mapping; mathematical theory of structure; neural network semantic theory; ontology; Artificial neural networks; Biological neural networks; Data mining; Feature extraction; Grounding; Leg; Neural networks; Neurons; Neuroscience; Ontologies;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Neural Networks, 2007. IJCNN 2007. International Joint Conference on
Conference_Location :
Orlando, FL
ISSN :
1098-7576
Print_ISBN :
978-1-4244-1379-9
Electronic_ISBN :
1098-7576
Type :
conf
DOI :
10.1109/IJCNN.2007.4371269
Filename :
4371269
Link To Document :
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