ML Anthology

Refining Numerical Constants in First Order Logic Theories

Marco Botta, Roberto Piola
MLJ 2000 pp. 109-131
doi:10.1023/A:1007686007399 /mlj/2000/botta2000mlj-refining/

Abstract

This paper proposes a method for refining numerical constants occurring in rules of a knowledge base expressed in a first order logic language. The method consists in tuning numerical parameters by performing error gradient descent. The knowledge base to be refined can be manually handcrafted or automatically acquired by a symbolic relational learner, able to deal with numerical features. The results of an experimental analysis performed on four case studies show that the refinement step can be effective in improving classification performances.

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Text

Botta and Piola. "Refining Numerical Constants in First Order Logic Theories." Machine Learning, 2000. doi:10.1023/A:1007686007399

Markdown

[Botta and Piola. "Refining Numerical Constants in First Order Logic Theories." Machine Learning, 2000.](https://mlanthology.org/mlj/2000/botta2000mlj-refining/) doi:10.1023/A:1007686007399

BibTeX

@article{botta2000mlj-refining,
  title     = {{Refining Numerical Constants in First Order Logic Theories}},
  author    = {Botta, Marco and Piola, Roberto},
  journal   = {Machine Learning},
  year      = {2000},
  pages     = {109-131},
  doi       = {10.1023/A:1007686007399},
  volume    = {38},
  url       = {https://mlanthology.org/mlj/2000/botta2000mlj-refining/}
}