0410599 20240103182225.520060210235959.9 3-540-42284-6 Berlin Springer 2001 10 s. Lecture Notes in Computer Science. 2096 168-177KittlerJ.RoliF. pattern recognition finite mixtures multiple solutions cav_un_auth*0101091 Grim Jiří UTIA-B Department of Pattern Recognition Ústav teorie informace a automatizace AV ČR, v. v. i. cav_un_auth*0021087 Kittler J. GB cav_un_auth*0101182 Pudil Pavel UTIA-B Department of Pattern Recognition Ústav teorie informace a automatizace AV ČR, v. v. i. cav_un_auth*0101197 Somol Petr UTIA-B Department of Pattern Recognition Ústav teorie informace a automatizace AV ČR, v. v. i. 12B GA402/01/0981 GA ČR cav_un_auth*0008962 KSK1019101 GA AV ČR cav_un_auth*0000219 AV0Z1075907 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. cav_un_auth*0212782 Multiple Classifier Systems. MCS 2001 Cambridge GB 02.07.2001-04.07.2001 BB RO http://hdl.handle.net/11104/0130688 UTIA-B 20010068 2001 2001 Berlin Springer 3-540-42284-6 Multiple Classifier Systems 168 177 Kittler J. 340 Roli F. 340