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Boosted Mixture of Experts: An Ensemble Learning Scheme.
- Source :
-
Neural Computation . 02/15/99, Vol. 11 Issue 2, p483-497. 15p. 1 Black and White Photograph, 1 Graph. - Publication Year :
- 1999
-
Abstract
- We present a new supervised learning procedure for ensemble machines, in which outputs of predictors, trained on different distributions, are combined by a dynamic classifier combination model. This procedure may be viewed as either a version of mixture of experts (Jacobs, Jordan, Nowlan, & Hinton, 1991), applied to classification, or a variant of the boosting algorithm (Schapire, 1990). As a variant of the mixture of experts, it can be made appropriate for general classification and regression problems by initializing the partition of the data set to different experts in a boostlike manner. If viewed as a variant of the boosting algorithm, its main gain is the use of a dynamic combination model for the outputs of the networks. Results are demonstrated on a synthetic example and a digit recognition task from the NIST database and compared with classical ensemble approaches. [ABSTRACT FROM AUTHOR]
- Subjects :
- *MACHINE learning
*COMPUTATIONAL learning theory
*ALGORITHMS
Subjects
Details
- Language :
- English
- ISSN :
- 08997667
- Volume :
- 11
- Issue :
- 2
- Database :
- Academic Search Index
- Journal :
- Neural Computation
- Publication Type :
- Academic Journal
- Accession number :
- 1555012
- Full Text :
- https://doi.org/10.1162/089976699300016737