Combining Explanation-Based Learning and Artificial Neural Networks

Shavlik, Jude W.; Towell, Geoffrey G.

doi:10.1016/B978-1-55860-036-2.50032-1

Combining Explanation-Based Learning and Artificial Neural Networks

Jude W. Shavlik, Geoffrey G. Towell

ICML 1989 pp. 90-93

doi:10.1016/B978-1-55860-036-2.50032-1 /icml/1989/shavlik1989icml-combining/

Abstract

Explanation-based and neural learning algorithms each have their own strengths and weaknesses. An approach for combining these two styles of learning is presented. Rather than being arbitrarily chosen, initial neural network configurations are determined by the generalized explanation of the solutions to specific tasks. If these generalized explanations are not completely correct, the neural network refines them. Empirical tests with an initial implementation of the approach demonstrate that the hybrid out-performs each of the individual algorithms alone.

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Cite

Text

Shavlik and Towell. "Combining Explanation-Based Learning and Artificial Neural Networks." International Conference on Machine Learning, 1989. doi:10.1016/B978-1-55860-036-2.50032-1

Markdown

[Shavlik and Towell. "Combining Explanation-Based Learning and Artificial Neural Networks." International Conference on Machine Learning, 1989.](https://mlanthology.org/icml/1989/shavlik1989icml-combining/) doi:10.1016/B978-1-55860-036-2.50032-1

BibTeX

@inproceedings{shavlik1989icml-combining,
  title     = {{Combining Explanation-Based Learning and Artificial Neural Networks}},
  author    = {Shavlik, Jude W. and Towell, Geoffrey G.},
  booktitle = {International Conference on Machine Learning},
  year      = {1989},
  pages     = {90-93},
  doi       = {10.1016/B978-1-55860-036-2.50032-1},
  url       = {https://mlanthology.org/icml/1989/shavlik1989icml-combining/}
}