### Citations

6460 | Neural networks and pattern recognition - Bishop - 1995 |

3550 | Bagging predictors
- Breiman
- 1996
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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
- Freund, Schapire
- 1997
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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
- Jain, Duin, et al.
- 2000
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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 |

873 | Hierarchical mixtures of experts and the EM algorithm - Jordan, Jacobs - 1994 |

850 | The strength of weak learnability
- Schapire
- 1990
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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
- Wolpert
- 1992
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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... |

667 | Neural networks ensembles - Hansen, Salamon - 1990 |

600 | An experimental comparison of three methods for constructing ensemble of decision trees - Dietterich |

570 | The random subspace method for constructing decision forests
- Ho
- 1998
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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... |

556 | Reducing multiclass to binary: A unifying approach for margin classifiers - Allwein, Schapire, et al. - 2000 |

508 | Boosting a Weak Learning Algorithm by Majority - Freund - 1995 |

471 | Methods of combining multiple classifiers and their applications to handwriting recognition - Xu, Krzyzak, et al. - 1992 |

468 | Neural network ensembles, cross validation, and active learning - Krogh, Vedelsby - 1995 |

370 | Decision Combination in Multiple Classifier Systems - Ho, Hull, et al. - 1994 |

347 | When networks disagree: Ensemble methods for hybrid neural networks - Perrone, Cooper - 1993 |

310 | Bayesian Model Averaging for Linear Regression Models - Raftery, Madigan, et al. |

283 | Popular ensemble methods: An empirical study - Opitz, Maclin - 1999 |

276 | Bayesian model averaging: A tutorial - Hoeting, Madigan, et al. - 1999 |

231 | Measures of diversity in classifier ensembles and their relationship with the ensemble accuracy - Kuncheva, Whitaker |

171 | Combination of Multiple Classifiers using Local Accuracy Estimates - Woods, Bowyer, et al. - 1997 |

161 | Combining classifiers in text categorization - Larkey, Croft - 1996 |

127 | A theoretical study on six classifier fusion strategies - Kuncheva |

114 | Evolutionary Ensembles with Negative Correlation Learning - Liu, Yao, et al. |

108 | Combining multiple classifiers by averaging or by multiplying - Tax, Breukelen, et al. |

99 | Introduction to - Alpaydin - 2004 |

98 | Democracy in Neural Nets: Voting Schemes for Classification - Battiti, Colla - 1994 |

95 | Boosting algorithms: regularization, prediction and model fitting (with discussion - Bühlmann, Hothorn - 2007 |

91 | Combining classifiers: A theoretical framework. Number 1 - Kittler - 1998 |

88 | Analysis of decision boundaries in linearly combined neural classi - Tumer, Ghosh - 1996 |

82 | A mixture model for clustering ensembles - Topchy, Jain, et al. |

81 | Combining multiple weak clusterings - Topchy, Topchy, et al. - 2003 |

79 | Ensemble learning via negative correlation - Liu, Yao - 1999 |

66 | Learning with Ensembles: How Overfitting can be Useful - Sollich, Krogh - 1996 |

65 | Sum versus Vote Fusion in Multiple Classifier Systems - Kittler, Alkoot |

65 | Optimal combinations of pattern classifiers - Lam, Suen - 1995 |

63 | Model selection and model averaging - CLAESKENS, HJORT - 2008 |

61 | Ensemble learning - Dietterich - 2002 |

61 | Kuncheva: Relationships between combination methods and measures of diversity in combining classifiers - Shipp |

56 | Ensembles of learning machines - Valentini, Masulli - 2002 |

55 | A theoretical and experimental analysis of linear combiners for multiple classifier systems - Fumera, Roli - 2005 |

55 | Is independence good for combining classifiers - Kuncheva, Whitaker, et al. |

55 | How boosting the margin can also boost classifier complexity - Reyzin, Schapire - 2006 |

52 | Switching between selection and fusion in combining classifiers: an experiment - Kuncheva - 2002 |

49 | Least squares model averaging - Hansen - 2007 |

48 | Learning ensembles from bites: A scalable and accurate - Chawla, Hall, et al. - 2004 |

47 | Multiple network fusion using fuzzy logic, Neural Networks - Cho, Kim - 1995 |

46 | Designing classifier fusion systems by genetic algorithms - Kuncheva, Jain - 2000 |

46 | Multi-label classification using ensembles of pruned sets - Read, Pfahringer, et al. - 2008 |

45 | Cooperative coevolution of artificial neural network ensembles for pattern classification - Garcı́a-Pedrajas, Hervás-Martı́nez, et al. - 2005 |

45 | Bagging, boosting and the random subspace method for linear classifiers - Skurichina, Duin - 2002 |

43 | B.: Classifier selection for majority voting - Ruta, Gabrys - 2005 |

42 | Stochastic Discrimination - Kleinberg - 1990 |

41 | Limits on the majority vote accuracy in classifier fusion - Kuncheva, Whitaker, et al. - 2003 |

37 | Evidence contrary to the statistical view of boosting, The - Mease, Wyner |

37 | Creating diversity in ensembles using artificial data - Melville, Mooney - 2005 |

36 | Bias-variance analysis of support vector machines for the development of SVM-based ensemble methods - Valentini, Dietterich - 2004 |

35 | Moderate diversity for better cluster ensembles - Hadjitodorov, Kuncheva, et al. - 2006 |

33 | On the equivalence of weak learnability and linear separability: New relaxations and efficient boosting algorithms - Shalev-Shwartz, Singer - 2008 |

26 | Evaluation of stability of k-means cluster ensembles with respect to random initialization - Kuncheva, Vetrov |

26 | Machine Learning An Algorithmic Perspective - Marsland - 2009 |

25 | Leave one out error, stability, and generalization of voting combinations of classifiers - Evgeniou, Pontil, et al. - 2004 |

23 | Weighted cluster ensembles: Methods and analysis - Domeniconi, Al-Razgan |

23 | On the algorithmic implementation of stochastic discrimination - Kleinberg |

22 | Classifier ensembles with a random linear oracle - Kuncheva, Rodriguez - 2007 |

21 | 2010): “A comparison of two model averaging techniques with an application to growth empirics - Magnus, Powell, et al. |

20 |
Diversity in multiple classifier systems
- Kuncheva
- 2005
(Show Context)
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... |

17 | Evolving hybrid ensembles of learning machines for better generalisation. Neurocomputing - Chandra, Yao - 2006 |

17 | On robustness of on-line boosting - a competitive study - Leistner, Saffari, et al. - 2009 |

15 | Performance Analysis and Comparison of Linear Combiners for Classifier Fusion - Fumera, Roli - 2002 |

14 | Strategies for teaching layered networks classification tasks - Wittner, Denker - 1988 |

13 | Critic-driven ensemble classification - Miller, Yan - 1999 |

9 | Incremental construction of classifier and discriminant ensembles - Ulas, Semerci, et al. - 2009 |

8 | Predictive learning via rule ensembles. The Annals of Applied Statistics - Friedman, Popescu - 2008 |

6 | Is combining classifiers with stacking better than selecting the best one - Dˇzeroski, ˇZenko - 2004 |

5 | Investigating the influence of the choice of the ensemble members in accuracy and diversity of selection-based and fusion-based methods for ensembles - Canuto, Abreu, et al. |

4 | Bagging for gaussian process regression. Neurocomputing 72(7):1605–1610 - Chen, Ren - 2009 |

4 | Problem-based learning - Rothman, Page - 2002 |

4 | An investigation of the effects of correlation, autocorrelation, and sample size in classifier fusion - Leap - 2004 |

2 | Feature selection for ensembles. In: American Association for ARTIFICIAL - OPITZ - 1999 |

2 | Stabilizing Weak Classifiers: Regularization and Combining Techniques in Discriminant Analysis - SKURICHINA - 2001 |

1 | 8 Learning Martin Sewell Bühlmann - Page - 2010 |

1 | 9 Learning Martin Sewell Hido - Page - 2009 |

1 | 11 Learning Martin Sewell - Page - 2010 |