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Ensemble Approaches in Learning| old_uid | 10061 |
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| title | Ensemble Approaches in Learning |
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| start_date | 2015/10/22 |
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| schedule | 10h |
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| online | no |
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| location_info | 25-26 |
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| summary | Designing a monolithic system for a large and complex learning task is hard.
Divide-and-conquer is a common strategy in tackling such large and complex
problems. Ensembles can be regarded an automatic approach towards automatic
divide-and-conquer. Many ensemble methods, including boosting, bagging,
negative correlation, etc., have been used in machine learning and data mining
for many years. This talk will describe three examples of ensemble methods,
i.e., multi-objective learning, online learning with concept drift, and
multi-class imbalance learning. Given the important role of diversity in
ensemble methods, some discussions and analyses will be given to gain a better
understanding of how and when diversity may help ensemble learning. |
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| responsibles | Piwowarski |
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