Chi-squared: A simpler evaluation function for multiple-instance learning

Abstract

This paper introduces a new evaluation function for solving the multiple instance problem. Our approach makes use of the main idea of diverse density (Maron, 1998; Maron & Lozano- Perez, 1998) but finds the best concept using the chi-square statistic. This approach is simpler than diverse density and allows us to search more extensively by using properties of the contingency table to prune in a guaranteed manner. We demonstrate that this approach solves the multiple-instance problem as well as or better than diverse density and that the pruning mechanism allows chi-squared to identify the best concepts more quickly.

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Document Details

Document Type
Technical Report
Publication Date
Jan 01, 2003
Accession Number
ADA465740

Entities

People

  • Amy Mcgovern
  • David Jensen

Organizations

  • University of Massachusetts Amherst

Tags

Communities of Interest

  • Autonomy

DTIC Thesaurus Topics

  • Accuracy
  • Algorithms
  • Artificial Intelligence
  • Computer Science
  • Data Sets
  • Equations
  • Genetic Algorithms
  • Information Processing
  • Information Systems
  • Learning
  • Machine Learning
  • Massachusetts
  • Probability
  • Reasoning
  • Reinforcement Learning
  • Supervised Machine Learning
  • Test And Evaluation

Fields of Study

  • Computer science

Readers

  • Operations Research
  • Regression Analysis.
  • Theoretical Analysis.