In many classification systems, sensing modalities have different acquisition costs. It is often
unnecessary to use every modality to classify a majority of examples. We study a multi-stage …

Significant changes in the instance distribution or associated cost function of a learning
problem require one to reoptimize a previously-learned classifier to work under new …

Multiple classifier methods are effective solutions to difficult pattern recognition problems.
However, empirical successes and failures have not been completely explained. Amid the …

Conventional methods for classifier design often suffer from having two conflicting goals-to
develop arbitrarily complex decision boundaries to suit a given problem, and at the same …

Dynamic Planning for Classifier Systems | Proceedings of the 5th International Conference on
Genetic Algorithms skip to main content ACM Digital Library home ACM home Google, Inc …

One-class classification is a type of unsupervised learning task wherein only information on
the target class is available and that on other classes is not. As this corresponds to many …

Combining multiple classifiers promises to increase performance and robustness of a
classification task. Currently, the understanding which combination scheme should be used …

More than a decade ago, combining multiple classifiers was proposed as a possible solution
to the problems posed by the traditional pattern classification approach which involved …

In difficult classification problems, the performance of a single classifier is often inadequate.
In such cases, several researchers have combined the outputs of multiple classifiers to …

We derive an upper bound on the generalization error of classifiers which can be
represented as thresholded convex combinations of thresholded convex combinations of …