LEADER 05673nam 22008175 450 001 9910143901403321 005 20251202141006.0 010 $a3-540-45428-4 024 7 $a10.1007/3-540-45428-4 035 $a(CKB)1000000000211768 035 $a(SSID)ssj0000325035 035 $a(PQKBManifestationID)11211312 035 $a(PQKBTitleCode)TC0000325035 035 $a(PQKBWorkID)10321026 035 $a(PQKB)10821859 035 $a(DE-He213)978-3-540-45428-1 035 $a(MiAaPQ)EBC3073174 035 $a(PPN)155208500 035 $a(BIP)13638518 035 $a(BIP)7818574 035 $a(EXLCZ)991000000000211768 100 $a20121227d2002 u| 0 101 0 $aeng 135 $aurnn#008mamaa 181 $ctxt 182 $cc 183 $acr 200 10$aMultiple Classifier Systems $eThird International Workshop, MCS 2002, Cagliari, Italy, June 24-26, 2002. Proceedings /$fedited by Fabio Roli, Josef Kittler 205 $a1st ed. 2002. 210 1$aBerlin, Heidelberg :$cSpringer Berlin Heidelberg :$cImprint: Springer,$d2002. 215 $a1 online resource (X, 342 p.) 225 1 $aLecture Notes in Computer Science,$x1611-3349 ;$v2364 300 $aBibliographic Level Mode of Issuance: Monograph 311 08$a3-540-43818-1 320 $aIncludes bibliographical references at the end of each chapters and index. 327 $aInvited Papers -- Multiclassifier Systems: Back to the Future -- Support Vector Machines, Kernel Logistic Regression and Boosting -- Multiple Classification Systems in the Context of Feature Extraction and Selection -- Bagging and Boosting -- Boosted Tree Ensembles for Solving Multiclass Problems -- Distributed Pasting of Small Votes -- Bagging and Boosting for the Nearest Mean Classifier: Effects of Sample Size on Diversity and Accuracy -- Highlighting Hard Patterns via AdaBoost Weights Evolution -- Using Diversity with Three Variants of Boosting: Aggressive, Conservative, and Inverse -- Ensemble Learning and Neural Networks -- Multistage Neural Network Ensembles -- Forward and Backward Selection in Regression Hybrid Network -- Types of Multinet System -- Discriminant Analysis and Factorial Multiple Splits in Recursive Partitioning for Data Mining -- Design Methodologies -- New Measure of Classifier Dependency in Multiple Classifier Systems -- A Discussion on the Classifier Projection Space for Classifier Combining -- On the General Application of the Tomographic Classifier Fusion Methodology -- Post-processing of Classifier Outputs in Multiple Classifier Systems -- Combination Strategies -- Trainable Multiple Classifier Schemes for Handwritten Character Recognition -- Generating Classifier Ensembles from Multiple Prototypes and Its Application to Handwriting Recognition -- Adaptive Feature Spaces for Land Cover Classification with Limited Ground Truth Data -- Stacking with Multi-response Model Trees -- On Combining One-Class Classifiers for Image Database Retrieval -- Analysis and Performance Evaluation -- Bias?Variance Analysis and Ensembles of SVM -- An Experimental Comparison of Fixed and Trained Fusion Rules for Crisp Classifier Outputs -- Reduction of the Boasting Bias of Linear Experts.-Analysis of Linear and Order Statistics Combiners for Fusion of Imbalanced Classifiers -- Applications -- Boosting and Classification of Electronic Nose Data -- Content-Based Classification of Digital Photos -- Classifier Combination for In Vivo Magnetic Resonance Spectra of Brain Tumours -- Combining Classifiers of Pesticides Toxicity through a Neuro-fuzzy Approach -- A Multi-expert System for Movie Segmentation -- Decision Level Fusion of Intramodal Personal Identity Verification Experts -- An Experimental Comparison of Classifier Fusion Rules for Multimodal Personal Identity Verification Systems. 330 $aThis book constitutes the refereed proceedings of the Third International Workshop on Multiple Classifier Systems, MCS 2002, held in Cagliari, Italy, in June 2002.The 29 revised full papers presented together with three invited papers were carefully reviewed and selected for inclusion in the volume. The papers are organized in topical sections on bagging and boosting, ensemble learning and neural networks, design methodologies, combination strategies, analysis and performance evaluation, and applications. 410 0$aLecture Notes in Computer Science,$x1611-3349 ;$v2364 606 $aComputer engineering 606 $aComputer networks 606 $aArtificial intelligence 606 $aPattern recognition systems 606 $aComputer vision 606 $aAlgorithms 606 $aComputer Engineering and Networks 606 $aArtificial Intelligence 606 $aAutomated Pattern Recognition 606 $aComputer Vision 606 $aAlgorithms 615 0$aComputer engineering. 615 0$aComputer networks. 615 0$aArtificial intelligence. 615 0$aPattern recognition systems. 615 0$aComputer vision. 615 0$aAlgorithms. 615 14$aComputer Engineering and Networks. 615 24$aArtificial Intelligence. 615 24$aAutomated Pattern Recognition. 615 24$aComputer Vision. 615 24$aAlgorithms. 676 $a006.3/1 702 $aRoli$b Fabio$4edt$4http://id.loc.gov/vocabulary/relators/edt 702 $aKittler$b Josef$4edt$4http://id.loc.gov/vocabulary/relators/edt 712 12$aInternational Workshop on Multiple Classifier Systems 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910143901403321 996 $aMultiple Classifier Systems$9772217 997 $aUNINA