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| 3550 | Bagging predictors
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(Show Context)
Citation Context ...ng, error-correcting output codes, bagging, boosting, mixtures of experts, stacked generalization and cascading. The taxonomy in Jain et al. (2000) is repeated in Table 1 (page 7). 4 Bagging Bagging (=-=Breiman, 1996-=-), a name derived from bootstrap aggregation, was the first effective method of ensemble learning and is one of the simplest methods of arching 1 . The meta-algorithm, which is a special case of model... |
| 3401 | A Decision-Theoretic Generalization of On-Line Learning and an Application to Boosting
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Citation Context ...ation in their mistakes reaches a combined performance that is significantly higher than the best obtainable from the individual nets. In 1995, Yoav Freund and Robert E. Schapire introduced AdaBoost (=-=Freund and Schapire, 1997-=-) (covered in Section 5.1 (page 8)). Cho and Kim (1995) combined the results from multiple neural networks using fuzzy logic which resulted in more accurate classification. Freund (1995) developed a m... |
| 2165 | Experiments with a new boosting algorithm - Freund, Schapire - 1996 |
| 1384 | On combining classifiers - Kittler, Hatef, et al. - 1998 |
| 998 | Statistical pattern recognition: a review
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Citation Context ...arable classifiers Random subspace Parallel Yes No Confidence Needs many comparable classifiers Neural trees Hierarchical Yes No Confidence Handles large numbers of classes Table 1: Ensemble methods (=-=Jain et al., 2000-=-) RN/11/02 Page 7Ensemble Learning Martin Sewell models are weighted according to their success and then the outputs are combined using voting (for classification) or averaging (for regression), thus... |
| 884 | Boosting the margin: A new explanation for the effectiveness of voting methods. The Annals of Statistics - Schapire, Freund, et al. - 1998 |
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| 850 | The strength of weak learnability
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Citation Context ... to create a single output. Bagging is only effective when using unstable (i.e. a small change in the training set can cause a significant change in the model) non-linear models. 5 Boosting Boosting (=-=Schapire, 1990-=-) is a meta-algorithm which can be viewed as a model averaging method. It is the most widely used ensemble method and one of the most powerful learning ideas introduced in the last twenty years. Origi... |
| 784 | A short introduction to boosting - Freund, Schapire - 1999 |
| 710 | Stacked generalization
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Citation Context ...It uses the same training set over and over again (thus it need not be large) and can also combine an arbitrary number of base-learners. 6 Stacked Generalization Stacked generalization (or stacking) (=-=Wolpert, 1992-=-) is a distinct way of combining multiple models, that introduces the concept of a meta learner. Although an attractive idea, it is less widely used than bagging and boosting. Unlike bagging and boost... |
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| 570 | The random subspace method for constructing decision forests
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Citation Context ...ochastic discrimination (SD). The method basically takes poor solutions as an input and creates good solutions. Stochastic discrimination looks promising, and later led to the random subspace method (=-=Ho, 1998-=-). Hansen and Salamon (1990) showed the benefits of invoking ensembles of similar neural networks. Wolpert (1992) introduced stacked generalization, a scheme for minimizing the generalization error ra... |
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Diversity in multiple classifier systems
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Citation Context ...e set of accurate and low-bias classifiers. In March 2005 the journal Information Fusion ran a special issue on ‘Diversity in multiple classifier systems’; Ludmila I. Kuncheva gave a guest editorial (=-=Kuncheva, 2005-=-). Melville and Mooney (2005) presented a new method RN/11/02 Page 4Ensemble Learning Martin Sewell for generating ensembles, DECORATE (Diverse Ensemble Creation by Oppositional Relabeling of Artific... |
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| 1 | 11 Learning Martin Sewell - Page - 2010 |
