A connectionist approach for building influence diagrams

The development of adaptive systems must face the problem of recognition as a synergy of learning and knowledge. This paper presents a method for constructing influence diagrams from backpropagation neural networks, as a way of combining the main advantages of these methodologies. The basic concepts...

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Bibliographic Details
Main Authors: Machado, A.M.C., Campos, M.F.M.
Format: Conference Proceeding
Language:English
Subjects:
Adaptive systems
Buildings
Computer science
Computer vision
Data mining
Face detection
Face recognition
Knowledge representation
Neural networks
Pattern recognition
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Summary:The development of adaptive systems must face the problem of recognition as a synergy of learning and knowledge. This paper presents a method for constructing influence diagrams from backpropagation neural networks, as a way of combining the main advantages of these methodologies. The basic concepts of influence diagrams and neural networks are discussed as a brief review. An algorithm to extract the conditional probabilities of the network is presented and illustrated by three pattern recognition examples. Although much of the a priori information from the sample set is lost during the training phase of the network, an influence diagram that behaves as the original knowledge source can be constructed.
DOI:10.1109/CYBVIS.1996.629442