ML Anthology

Shifting Vocabulary Bias in Speedup Learning

Devika Subramanian
MLJ 1995 pp. 155-191
doi:10.1007/BF00993478 /mlj/1995/subramanian1995mlj-shifting/

Abstract

In this paper, we describe a domain-independent principle for justified shifts of vocabulary bias in speedup learning. This principle advocates the minimization of wasted computational effort. It explains as well as generates a special class of granularity shifts. We describe its automation for definite as well as stratified Horn theories, and present an implementation for a general class of reachability computations.

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Text

Subramanian. "Shifting Vocabulary Bias in Speedup Learning." Machine Learning, 1995. doi:10.1007/BF00993478

Markdown

[Subramanian. "Shifting Vocabulary Bias in Speedup Learning." Machine Learning, 1995.](https://mlanthology.org/mlj/1995/subramanian1995mlj-shifting/) doi:10.1007/BF00993478

BibTeX

@article{subramanian1995mlj-shifting,
  title     = {{Shifting Vocabulary Bias in Speedup Learning}},
  author    = {Subramanian, Devika},
  journal   = {Machine Learning},
  year      = {1995},
  pages     = {155-191},
  doi       = {10.1007/BF00993478},
  volume    = {20},
  url       = {https://mlanthology.org/mlj/1995/subramanian1995mlj-shifting/}
}