Information analysis of multiple classifier fusion

0410599 - UTIA-B 20010068 RIV DE eng C - Conference Paper (international conference)
Grim, Jiří - Kittler, J. - Pudil, Pavel - Somol, Petr
Information analysis of multiple classifier fusion.
Berlin: Springer, 2001. Lecture Notes in Computer Science., 2096. ISBN 3-540-42284-6. In: Multiple Classifier Systems. - (Kittler, J.; Roli, F.), s. 168-177
[Multiple Classifier Systems. MCS 2001. Cambridge (GB), 02.07.2001-04.07.2001]
R&D Projects: GA ČR GA402/01/0981; GA AV ČR KSK1019101
Institutional research plan: AV0Z1075907
Keywords : pattern recognition * finite mixtures * multiple solutions
Subject RIV: BB - Applied Statistics, Operational Research

We consider a general scheme of parallel classifier combinations in the framework of statistical pattern recognition. Each statistical classifier defines a set of output variables in terms of a posteriori probabilities, i.e. it is used as a feature extractor. The output vectors of classifiers are combined in parallel. The statistical Shannon information is used as a criterion to compare different combining schemes. By means of relatively simple arguments we derive a hierarchy between different methods.
Permanent Link: http://hdl.handle.net/11104/0130688