ML Anthology

Training Diagraphs

Hsieh-Chang Tu, Carl H. Smith
ALT 1994 pp. 176-186
doi:10.1007/3-540-58520-6_63 /alt/1994/tu1994alt-training/

Abstract

A formal definition of what it means for a machine to learn a collection of concepts in an order determined by a finite acyclic digraph of recursive functions is presented. We show that given a labelled graph G =(V, E ) representing the learning structure, there are sets S such that in order to learn a program corresponding to some node i, a machine must have precisely learned programs corresponding to all the predecessor nodes.

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Cite

Text

Tu and Smith. "Training Diagraphs." International Conference on Algorithmic Learning Theory, 1994. doi:10.1007/3-540-58520-6_63

Markdown

[Tu and Smith. "Training Diagraphs." International Conference on Algorithmic Learning Theory, 1994.](https://mlanthology.org/alt/1994/tu1994alt-training/) doi:10.1007/3-540-58520-6_63

BibTeX

@inproceedings{tu1994alt-training,
  title     = {{Training Diagraphs}},
  author    = {Tu, Hsieh-Chang and Smith, Carl H.},
  booktitle = {International Conference on Algorithmic Learning Theory},
  year      = {1994},
  pages     = {176-186},
  doi       = {10.1007/3-540-58520-6_63},
  url       = {https://mlanthology.org/alt/1994/tu1994alt-training/}
}