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Integrating Feature Extraction and Memory Search
- Source :
- Machine Learning; March 1993, Vol. 10 Issue: 3 p311-339, 29p
- Publication Year :
- 1993
-
Abstract
- Reasoning from prior cases or abstractions requires that a system identify relevant similarities between the current situation and objects represented in memory. Often, relevance depends upon abstract, thematic, costly-to-infer properties of the situation. Because of the cost of inference, a case-retrieval system needs to learn which descriptions are worth inferring, and how costly tht inference will be. This article outlines the properties that make an abstract thematic feature valuable to a case-based reasoner, and recasts the problem of case retrieval into a framework under which a system can explicitly and dynamically reason about the cost of acquiring features relative to their information value.
Details
- Language :
- English
- ISSN :
- 08856125 and 15730565
- Volume :
- 10
- Issue :
- 3
- Database :
- Supplemental Index
- Journal :
- Machine Learning
- Publication Type :
- Periodical
- Accession number :
- ejs14994322
- Full Text :
- https://doi.org/10.1023/A:1022691111431