Multiple Classifier Systems

Multiple Classifier Systems

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The fusion of di?erent information sourcesis a persistent and intriguing issue. It hasbeenaddressedforcenturiesinvariousdisciplines, includingpoliticalscience, probability and statistics, system reliability assessment, computer science, and distributed detection in communications. Early seminal work on fusion was c- ried out by pioneers such as Laplace and von Neumann. More recently, research activities in information fusion have focused on pattern recognition. During the 1990s, classi?erfusionschemes, especiallyattheso-calleddecision-level, emerged under a plethora of di?erent names in various scienti?c communities, including machine learning, neural networks, pattern recognition, and statistics. The d- ferent nomenclatures introduced by these communities re?ected their di?erent perspectives and cultural backgrounds as well as the absence of common forums and the poor dissemination of the most important results. In 1999, the ?rst workshop on multiple classi?er systems was organized with the main goal of creating a common international forum to promote the diss- ination of the results achieved in the diverse communities and the adoption of a common terminology, thus giving the di?erent perspectives and cultural ba- grounds some concrete added value. After ?ve meetings of this workshop, there is strong evidence that signi?cant steps have been made towards this goal. - searchers from these diverse communities successfully participated in the wo- shops, and world experts presented surveys of the state of the art from the perspectives of their communities to aid cross-fertilizat... algorithm was then modified to accommodate the SVM classifiers, which were implemented in Matlab using the toolbox provided by [19]. ... RBF kernels were used since they consistently yielded better performance than polynomial kernels. ... The code matrices were generated by: a€c The Exhaustive method in [6] when the number of classes kalt;=7. ... From the above table, it can be seen that the use of SVM as the internal classifier improves the performance of the BHC significantly.

Title:Multiple Classifier Systems
Author:Fabio Roli, Josef Kittler, Terry Windeatt
Publisher:Springer Science & Business Media - 2004-06-01


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