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2002

Specific-to-General Learning for Temporal Events with Application to Learning Event Definitions from Video

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Specific-to-General Learning for Temporal Events with Application to Learning Event Definitions from Video
We develop, analyze, and evaluate a novel, supervised, specific-to-general learner for a simple temporal logic and use the resulting algorithm to learn visual event definitions from video sequences. First, we introduce a simple, propositional, temporal, event-description language called AMA that is sufficiently expressive to represent many events yet sufficiently restrictive to support learning. We then give algorithms, along with lower and upper complexity bounds, for the subsumption and generalization problems for AMA formulas. We present a positive-examples
Alan Fern, Robert Givan, Jeffrey Mark Siskind
Added 22 Dec 2010
Updated 22 Dec 2010
Type Journal
Year 2002
Where JAIR
Authors Alan Fern, Robert Givan, Jeffrey Mark Siskind
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