Integrating Feature Extraction and Memory Search

@article{Owens1993IntegratingFE,
  title={Integrating Feature Extraction and Memory Search},
  author={Christopher C. Owens},
  journal={Machine Learning},
  year={1993},
  volume={10},
  pages={311-339}
}
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… CONTINUE READING
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