Online Policy Adaptation for Ensemble Algorithms
| Type of publication: | Idiap-RR |
| Citation: | DimitrakBengio2002a |
| Number: | Idiap-RR-28-2002 |
| Year: | 2002 |
| Institution: | IDIAP |
| Abstract: | Ensemble algorithms are general methods for improving the performance of a given learning algorithm. This is achieved by the combination of multiple base classifiers into an ensemble. In this paper, the idea of using an adaptive policy for training and combining the base classifiers is put forward. The effectiveness of this approach for online learning is demonstrated by experimental results. |
| Userfields: | ipdmembership={learning}, |
| Keywords: | |
| Projects |
Idiap |
| Authors | |
| Added by: | [UNK] |
| Total mark: | 0 |
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