Fast Learning in Multi-Resolution Hierarchies

@inproceedings{Moody1988FastLI,
  title={Fast Learning in Multi-Resolution Hierarchies},
  author={John E. Moody},
  booktitle={NIPS},
  year={1988}
}
A class of fast, supervised learning algorithms is presented. They use local representations, hashing, atld multiple scales of resolution to approximate functions which are piece-wise continuous. Inspired by Albus's CMAC model, the algorithms learn orders of magnitude more rapidly than typical implementations of back propagation, while often achieving comparable qualities of generalization. Furthermore, unlike most traditional function approximation methods, the algorithms are well suited for… CONTINUE READING
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