Improving learning performance through rational resource allocation

Jonathan Gratch, Steve Chien, Gerald F DeJong

Research output: Contribution to conferencePaper

Abstract

This article shows how rational analysis can be used to minimize learning cost for a general class of statistical learning problems. We discuss the factors that influence learning cost and show that the problem of efficient learning can be cast as a resource optimization problem. Solutions found in this way can be significantly more efficient than the best solutions that do not account for these factors. We introduce a heuristic learning algorithm that approximately solves this optimization problem and document its performance improvements on synthetic and real-world problems.

Original languageEnglish (US)
Pages576-581
Number of pages6
StatePublished - Dec 1 1994
EventProceedings of the 12th National Conference on Artificial Intelligence. Part 1 (of 2) - Seattle, WA, USA
Duration: Jul 31 1994Aug 4 1994

Other

OtherProceedings of the 12th National Conference on Artificial Intelligence. Part 1 (of 2)
CitySeattle, WA, USA
Period7/31/948/4/94

ASJC Scopus subject areas

  • Software
  • Artificial Intelligence

Fingerprint Dive into the research topics of 'Improving learning performance through rational resource allocation'. Together they form a unique fingerprint.

  • Cite this

    Gratch, J., Chien, S., & DeJong, G. F. (1994). Improving learning performance through rational resource allocation. 576-581. Paper presented at Proceedings of the 12th National Conference on Artificial Intelligence. Part 1 (of 2), Seattle, WA, USA, .