Learning from Queries and Examples with Tree-Structured Bias

Tadepalli, Prasad

doi:10.1016/B978-1-55860-307-3.50048-4

Learning from Queries and Examples with Tree-Structured Bias

Prasad Tadepalli

ICML 1993 pp. 322-329

doi:10.1016/B978-1-55860-307-3.50048-4 /icml/1993/tadepalli1993icml-learning/

Abstract

Incorporating declarative bias or prior knowledge into learning is an active research topic in machine learning. Tree-structured bias specifies the prior knowledge as a tree of “relevance” relationships between attributes. This paper presents a learning algorithm that implements tree-structured bias, i.e., learns any target function probably approximately correctly from random examples and membership queries if it obeys a given tree-structured bias. The theoretical predictions of the paper are empirically validated.

ICML Semantic Scholar

Cite

Text

Tadepalli. "Learning from Queries and Examples with Tree-Structured Bias." International Conference on Machine Learning, 1993. doi:10.1016/B978-1-55860-307-3.50048-4

Markdown

[Tadepalli. "Learning from Queries and Examples with Tree-Structured Bias." International Conference on Machine Learning, 1993.](https://mlanthology.org/icml/1993/tadepalli1993icml-learning/) doi:10.1016/B978-1-55860-307-3.50048-4

BibTeX

@inproceedings{tadepalli1993icml-learning,
  title     = {{Learning from Queries and Examples with Tree-Structured Bias}},
  author    = {Tadepalli, Prasad},
  booktitle = {International Conference on Machine Learning},
  year      = {1993},
  pages     = {322-329},
  doi       = {10.1016/B978-1-55860-307-3.50048-4},
  url       = {https://mlanthology.org/icml/1993/tadepalli1993icml-learning/}
}