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Incremental learning with rule-based neural networks

Higgins, C. M. and Goodman, R. M. (1991) Incremental learning with rule-based neural networks. In: IJCNN-91-Seattle International Joint Conference on Neural Networks. Vol.1. IEEE , Piscataway, NJ, pp. 875-880. ISBN 0780301641.

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A classifier for discrete-valued variable classification problems is presented. The system utilizes an information-theoretic algorithm for constructing informative rules from example data. These rules are then used to construct a neural network to perform parallel inference and posterior probability estimation. The network can be grown incrementally, so that new data can be incorporated without repeating the training on previous data. It is shown that this technique performs as well as other techniques such as backpropagation while having unique advantages in incremental learning capability, training efficiency, knowledge representation, and hardware implementation suitability.

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Additional Information:© 1991 IEEE. This work was supported in part by the Army Research Office under contract number DAAL03-89-K-0126, and in part by DARPA under contract number AFOSR-90-0199.
Funding AgencyGrant Number
Army Research Office (ARO)DAAL03-89-K-0126
Defense Advanced Research Projects Agency (DARPA)UNSPECIFIED
Air Force Office of Scientific Research (AFOSR)AFOSR-90-0199
Record Number:CaltechAUTHORS:20190314-142000764
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Official Citation:C. M. Higgins and R. M. Goodman, "Incremental learning with rule-based neural networks," IJCNN-91-Seattle International Joint Conference on Neural Networks, Seattle, WA, USA, 1991, pp. 875-880 vol.1. doi: 10.1109/IJCNN.1991.155294
Usage Policy:No commercial reproduction, distribution, display or performance rights in this work are provided.
ID Code:93836
Deposited By: George Porter
Deposited On:14 Mar 2019 22:11
Last Modified:16 Nov 2021 17:01

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