Accession Number:

ADA255362

Title:

Machine Learning: Proceedings of the International Workshop (ML92) Held in Aberdeen, Scotland, on 1-3 July 1992,

Descriptive Note:

Corporate Author:

TEXAS UNIV AT AUSTIN ARTIFICIAL INTELLIGENCE LAB

Personal Author(s):

Report Date:

1992-07-01

Pagination or Media Count:

500.0

Abstract:

Most empirical evaluations of machine learning algorithms are case studies evaluations of multiple algorithms on multiple databases. Authors of case studies implicitly or explicitly hypothesize that the pattern of their results, which often suggests that one algorithm performs significantly better than others, is not limited to the small number of databases investigated, but instead holds for some general class of learning problems. However, these hypotheses are rarely supported with additional evidence, which leaves them suspect. This paper describes an empirical method for generalizing results from case studies and an example application. This method yields rules describing when some algorithms significantly outperform others on some dependent measures. Advantages for generalizing from case studies and limitations of this particular approach are also described.

Subject Categories:

  • Psychology
  • Cybernetics

Distribution Statement:

APPROVED FOR PUBLIC RELEASE