Ensemble methods are learning algorithms that construct a set of classifiers and then
classify new data points by taking a (weighted) vote of their predictions. The original
ensemble method is Bayesian averaging, but more recent algorithms include error- ...

During the past ten years, dualities of Pontryagin type have proliferated. One such duality is
that between the category D of bounded distributive lattices and the category P of compact
totally order disconnected spaces, which, at the object level, provides the natural ...

Abstract We develop a common theoretical framework for combining classifiers which use
distinct pattern representations and show that many existing schemes can be considered as
special cases of compound classification where all the pattern representations are used ...

In the field of pattern recognition, multiple classifier systems based on the combination of
outputs of a set of different classifiers have been proposed as a method for the development
of high performance classification systems. In this paper, the problem of design of multiple ...

Bagging and boosting are methods that generate a diverse ensemble of classifiers by
manipulating the training data given to a “base” learning algorithm. Breiman has pointed out
that they rely for their effectiveness on the instability of the base learning algorithm. An ...

Abstract Several means for improving the performance and training of neural networks for
classification are proposed. Crossvalidation is used as a tool for optimizing network
parameters and architecture. It is shown that the remaining residual generalization error ...

We present a tree-structured architecture for supervised learning. The statistical model
underlying the architecture is a hierarchical mixture model in which both the mixture
coefficients and the mixture components are generalized linear models (GLIM's). Learning ...

We consider strategies for building classifier ensembles for non-stationary environments
where the classification task changes during the operation of the ensemble. Individual
classifier models capable of online learning are reviewed. The concept of” forgetting” is ...

Abstract Learning of continuous valued functions using neural network ensembles
(committees) can give improved accuracy, reliable estimation of the generalization error,
and active learning. The ambiguity is defined as the variation of the output of ensemble ...

Abstract This paper introduces the problem of combining multiple partitionings of a set of
objects into a single consolidated clustering without accessing the features or algorithms
that determined these partitionings. We first identify several application scenarios for the ...