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KnowledgeBased Artificial Neural Networks
, 1994
"... Hybrid learning methods use theoretical knowledge of a domain and a set of classified examples to develop a method for accurately classifying examples not seen during training. The challenge of hybrid learning systems is to use the information provided by one source of information to offset informat ..."
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Cited by 185 (13 self)
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that the networks created by KBANN generalize better than a wide variety of learning systems, as well as several t...
Running Head: KnowledgeBased Artificial Neural Networks
"... correspondence to this address. Hybrid learning methods use theoretical knowledge of a domain and a set of classified examples to develop a method for accurately classifying examples not seen during training. The challenge of hybrid learning systems is to use the information provided by one source o ..."
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that the networks created by KBANN generalize better than a wide variety of learning systems, as well as several techniques proposed by biologists. ii 1.
Using Symbolic Learning to Improve KnowledgeBased Neural Networks
, 1992
"... The previouslyreported Kbann system integrates existing knowledge into neural networks by defining the network topology and setting initial link weights. Standard neural learning techniques can then be used to train such networks, thereby refining the information upon which the network is based. Ho ..."
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Cited by 20 (1 self)
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of networks to generalize correctly to testing examples. Introduction Kbann is a "hybrid" learning system; ...
Refining Algorithms with KnowledgeBased Neural Networks: Improving the ChouFasman Algorithm for Protein Folding
 in Computational Learning Theory and Natural Learning Systems
, 1992
"... We describe a method for using machine learning to refine algorithms represented as generalized finitestate automata. The knowledge in an automaton is translated into a corresponding artificial neural network, and then refined by applying backpropagation to a set of examples. Our technique for t ..."
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Cited by 16 (1 self)
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We describe a method for using machine learning to refine algorithms represented as generalized finitestate automata. The knowledge in an automaton is translated into a corresponding artificial neural network, and then refined by applying backpropagation to a set of examples. Our technique
Dynamically Adding Symbolically Meaningful Nodes to KnowledgeBased Neural Networks
 KNOWLEDGEBASED SYSTEMS
, 1995
"... Traditional connectionist theoryrefinement systems map the dependencies of a domainspecific rule base into a neural network, and then refine this network using neural learning techniques. Most of these systems, however, lack the ability to refine their network's topology and are thus unable to ..."
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Cited by 26 (4 self)
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to add new rules to the (reformulated) rule base. Therefore, on domain theories that are lacking rules, generalization is poor, and training can corrupt the original rules, even those that were initially correct. We present TopGen, an extension to the Kbann algorithm, that heuristically searches
Using KnowledgeBased Neural Networks to Improve Algorithms: Refining the ChouFasman Algorithm for Protein Folding
 Machine Learning
, 1993
"... We describe a method for using machine learning to refine algorithms represented as generalized finitestate automata. The knowledge in an automaton is translated into an artificial neural network, and then refined with backpropagation on a set of examples. Our technique for translating an automaton ..."
Abstract

Cited by 38 (5 self)
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We describe a method for using machine learning to refine algorithms represented as generalized finitestate automata. The knowledge in an automaton is translated into an artificial neural network, and then refined with backpropagation on a set of examples. Our technique for translating
Cascade ARTMAP: Integrating Neural Computation and Symbolic Knowledge Processing
 IEEE Transactions on Neural Networks
, 1997
"... Abstract — This paper introduces a hybrid system termed cascade adaptive resonance theory mapping (ARTMAP) that incorporates symbolic knowledge into neuralnetwork learning and recognition. Cascade ARTMAP, a generalization of fuzzy ARTMAP, represents intermediate attributes and rule cascades of rule ..."
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Cited by 36 (12 self)
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Abstract — This paper introduces a hybrid system termed cascade adaptive resonance theory mapping (ARTMAP) that incorporates symbolic knowledge into neuralnetwork learning and recognition. Cascade ARTMAP, a generalization of fuzzy ARTMAP, represents intermediate attributes and rule cascades
Heuristically Expanding KnowledgeBased Neural Networks
 In Proceedings of the Thirteenth International Joint Conference on Artificial Intelligence
, 1993
"... Knowledgebased neural networks are networks whose topology is determined by mapping the dependencies of a domainspecific rulebase into a neural network. However, existing network training methods lack the ability to add new rules to the (reformulated) rulebases. Thus, on domain theories that are l ..."
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Cited by 24 (11 self)
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that are lacking rules, generalization is poor, and training can corrupt the original rules, even those that were initially correct. We present TopGen, an extension to the Kbann algorithm, that heuristically searches for possible expansions of a knowledgebased neural network, guided by the domain theory
Constructive Induction in KnowledgeBased Neural Networks
 Machine Learning  Proceedings of the Eighth International Workshop
, 1991
"... Artificial neural networks have proven to be a successful, general method for inductive learning from examples. However, they have not often been viewed in terms of constructive induction. We describe a method for using a knowledgebased neural network of the kind created by the Kbann algorithm as t ..."
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Cited by 17 (3 self)
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Artificial neural networks have proven to be a successful, general method for inductive learning from examples. However, they have not often been viewed in terms of constructive induction. We describe a method for using a knowledgebased neural network of the kind created by the Kbann algorithm
Machine Learning Research Group Working Paper 912 Refining Algorithms with KnowledgeBased Neural Networks: Improving the ChouFasman Algorithm for Protein Folding *
"... We describe a method for using machine learning to refine algorithms represented as generalized finitestate automata. The knowledge in an automaton is translated into a corresponding artificial neural network, and then refined by applying backpropagation to a set of examples. Our technique for tran ..."
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We describe a method for using machine learning to refine algorithms represented as generalized finitestate automata. The knowledge in an automaton is translated into a corresponding artificial neural network, and then refined by applying backpropagation to a set of examples. Our technique
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