Ensemble methods : a review

@inproceedings{R2012EnsembleM,
  title={Ensemble methods : a review},
  author={Matteo R{\'e} and Giorgio Valentini},
  year={2012}
}
1 Ensemble methods: a review 3 Matteo Re and Giorgio Valentini 1. 

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References

SHOWING 1-10 OF 216 REFERENCES

Combining Artificial Neural Nets: Ensemble and Modular Multi-Net Systems

TLDR
This volume, written by leading researchers, presents methods of combining neural nets to improve their performance and suggests ways to improve the quality of these nets.

Stochastic discrimination

  • E. Kleinberg
  • Mathematics
    Annals of Mathematics and Artificial Intelligence
  • 2005
TLDR
A general method is introduced for separating points in multidimensional spaces through the use of stochastic processes, called Stochastic discrimination.

Ensemble selection from libraries of models

TLDR
A method for constructing ensembles from libraries of thousands of models using forward stepwise selection to be optimized to performance metric such as accuracy, cross entropy, mean precision, or ROC Area is presented.

Classification by Pairwise Coupling

TLDR
A strategy for polychotomous classification that involves estimating class probabilities for each pair of classes, and then coupling the estimates together is discussed, similar to the Bradley-Terry method for paired comparisons.

Focused Ensemble Selection: A Diversity-Based Method for Greedy Ensemble Selection

TLDR
Experimental comparison of the proposed method, dubbed Focused Ensemble Selection (FES), against state-of-the-art greedy ensemble selection methods shows that it leads to small ensembles with high predictive performance.

Ensemble Methods for Classification in Cheminformatics

We describe the application of ensemble methods to binary classification problems on two pharmaceutical compound data sets. Several variants of single and ensembles models of k-nearest neighbors

When Networks Disagree: Ensemble Methods for Hybrid Neural Networks

TLDR
Experimental results show that the ensemble method dramatically improves neural network performance on difficult real-world optical character recognition tasks.

A Survey: Clustering Ensembles Techniques

TLDR
The clustering ensembles combine multiple partitions generated by different clustering algorithms into a single clustering solution, representation of multiple partitions, its challenges and present taxonomy of combination algorithms.

Nonlinear independent component analysis using ensemble learning: Theory

TLDR
A nonlinear version of independent component analysis is presented, using a multi-layer perceptron network and the distributions of sources are modelled by mixtures of Gaussians to estimate the posterior probability of all the unknown parameters.
...