The Management of Context-Sensitive Features: A Review of Strategies

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dc.creatorTurney, Peter D.
dc.date2002-12-12
dc.date.accessioned2026-07-07T03:19:17Z
dc.date.available2026-07-07T03:19:17Z
dc.descriptionIn this paper, we review five heuristic strategies for handling context-sensitive features in supervised machine learning from examples. We discuss two methods for recovering lost (implicit) contextual information. We mention some evidence that hybrid strategies can have a synergetic effect. We then show how the work of several machine learning researchers fits into this framework. While we do not claim that these strategies exhaust the possibilities, it appears that the framework includes all of the techniques that can be found in the published literature on contextsensitive learning.
dc.description7 pages
dc.identifierhttps://arxiv.org/abs/cs/0212037
dc.identifierhttp://arxiv.org/abs/cs/0212037
dc.identifier13th International Conference on Machine Learning, Workshop on Learning in Context-Sensitive Domains, Bari, Italy, (1996), 60-66
dc.identifier.urihttp://salesiana.dossiersoluciones.com/handle/123456789/31396
dc.subjectMachine Learning
dc.subjectComputer Vision and Pattern Recognition
dc.subjectI.2.6; I.5.2
dc.titleThe Management of Context-Sensitive Features: A Review of Strategies
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