The diversity of an ensemble of classifiers can be calculated in a variety of ways. Here a
diversity metric and a means for altering the diversity of an ensemble, called “thinning”, are …

This paper proposes a method for combining multiple tree classifiers based on both
classifier ensemble (bagging) and dynamic classifier selection schemes (DCS). The …

Statistical dependence of classifiers has recently been shown to improve accuracy over
statistically independent classifiers. In this paper, we focus on the verification application …

In this paper, we present a theoretical study on automated understanding of the design
drawing. This can lead to design support through the natural interface of sketching. In earlier …

In this paper we describe an approach for spoken language analysis for helpdesk call
routing using a combination of simple recurrent networks and support vector machines. In …

Land cover map development was done on the base of the Landsat 7 ETM+ images. As a
background topographical map of the scale 1: 100 000 was used. Satellite image …

Summary form only given. Existing studies on ensemble classifiers typically take a static
approach in assembling individual classifiers, in which all the important features are …

Combining multiple classifiers have focused mainly on combination methods, but a few
studies have investigated on how to select component classifiers from a classifier pool …

The advent of digital technology and the Internet requires an efficient approach to the
retrieval and indexing of large volumes of diverse images. Traditional content-based image …

Error and uncertainty in remotely sensed data come from several sources, and can be
increased or mitigated by the processing to which that data is subjected. For example, the …