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Abstract: Event prediction is an important problem with many realworld applications. For the majority of these applications, it is not necessary to predict the exact time an event will occur---it is acceptable to predict that the event will occur within some time interval. The use of this time interval introduces ambiguity into the event prediction problem, permitting it to be viewed as a multiple-instance learning problem. We have developed timeweaver, a genetic-algorithm based learning system that is... (Update)
Active bibliography (related documents): More All
0.3: Pattern Extraction for Time Series Classification - Geurts (2001) (Correct)
0.3: Temporal Machine Learning for Switching Control - Geurts, Wehenkel (2000) (Correct)
0.1: Timeweaver: a Genetic Algorithm for Identifying Predictive.. - Weiss (1999) (Correct)
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0.6: On Generalized Multiple-Instance Learning - Scott, Zhang (2003) (Correct)
0.3: Multiple Instance Learning with Generalized Support.. - Andrews, Hofmann.. (2002) (Correct)
0.3: Learning to Predict Extremely Rare Events - Weiss, Hirsh (2000) (Correct)
BibTeX entry: (Update)
@misc{ weiss-event,
author = "Gary M. Weiss and Haym Hirsh",
title = "Event Prediction: Learning from Ambiguous Examples",
url = "citeseer.ist.psu.edu/weiss98event.html" }
Citations (may not include all citations):20 Learning to Predict Rare Events in Event Sequences - Weiss, Hirsh - 1998
15 Discovering patterns in sequences of events - Dietterich, Michalski - 1985
3 Early Prediction of Electric Power System Blackouts by Tempo.. - Geurts, Wehenkel - 1998
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