A probabilistic framework for memory-based reasoning

Simon Kasif, Steven Salzberg, David Waltz, John Rachlin, David W. Aha

Research output: Contribution to journalArticlepeer-review

Abstract

In this paper, we propose a probabilistic framework for memory-based reasoning (MBR). The framework allows us to clarify the technical merits and limitations of several recently published MBR methods and to design new variants. The proposed computational framework consists of three components: a specification language to define an adaptive notion of relevant context for a query; mechanisms for retrieving this context; and local learning procedures that are used to induce the desired action from this context. We primarily focus on actions in the form of a classification. Based on the framework we derive several analytical and empirical results that shed light on MBR algorithms. We introduce the notion of an MBR transform, and discuss its utility for learning algorithms. We also provide several perspectives on memory-based reasoning from a multi-disciplinary point of view.

Original languageEnglish (US)
Pages (from-to)287-311
Number of pages25
JournalArtificial Intelligence
Volume104
Issue number1-2
DOIs
StatePublished - Sep 1998

Keywords

  • Bayes networks
  • Learning
  • Local learning
  • MBR transform
  • Memory-based learning
  • Meta-learning
  • Probabilistic inference

ASJC Scopus subject areas

  • Language and Linguistics
  • Linguistics and Language
  • Artificial Intelligence

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