LEADER 05876nam 22007935 450 001 9910767550803321 005 20250729100411.0 010 $a3-540-45014-9 024 7 $a10.1007/3-540-45014-9 035 $a(CKB)1000000000211272 035 $a(SSID)ssj0000325034 035 $a(PQKBManifestationID)11234405 035 $a(PQKBTitleCode)TC0000325034 035 $a(PQKBWorkID)10320833 035 $a(PQKB)10355186 035 $a(DE-He213)978-3-540-45014-6 035 $a(MiAaPQ)EBC3072852 035 $a(PPN)155193597 035 $a(BIP)13627355 035 $a(BIP)6535028 035 $a(EXLCZ)991000000000211272 100 $a20121227d2000 u| 0 101 0 $aeng 135 $aurnn#008mamaa 181 $ctxt 182 $cc 183 $acr 200 10$aMultiple Classifier Systems $eFirst International Workshop, MCS 2000 Cagliari, Italy, June 21-23, 2000 Proceedings /$fedited by Josef Kittler, Fabio Roli 205 $a1st ed. 2000. 210 1$aBerlin, Heidelberg :$cSpringer Berlin Heidelberg :$cImprint: Springer,$d2000. 215 $a1 online resource (XII, 408 p.) 225 1 $aLecture Notes in Computer Science,$x1611-3349 ;$v1857 300 $aBibliographic Level Mode of Issuance: Monograph 311 08$a3-540-67704-6 320 $aIncludes bibliographical references at the end of each chapters and index. 327 $aEnsemble Methods in Machine Learning -- Experiments with Classifier Combining Rules -- The ?Test and Select? Approach to Ensemble Combination -- A Survey of Sequential Combination of Word Recognizers in Handwritten Phrase Recognition at CEDAR -- Multiple Classifier Combination Methodologies for Different Output Levels -- A Mathematically Rigorous Foundation for Supervised Learning -- Classifier Combinations: Implementations and Theoretical Issues -- Some Results on Weakly Accurate Base Learners for Boosting Regression and Classification -- Complexity of Classification Problems and Comparative Advantages of Combined Classifiers -- Effectiveness of Error Correcting Output Codes in Multiclass Learning Problems -- Combining Fisher Linear Discriminants for Dissimilarity Representations -- A Learning Method of Feature Selection for Rough Classification -- Analysis of a Fusion Method for Combining Marginal Classifiers -- A hybrid projection based and radial basis function architecture -- Combining Multiple Classifiers in Probabilistic Neural Networks -- Supervised Classifier Combination through Generalized Additive Multi-model -- Dynamic Classifier Selection -- Boosting in Linear Discriminant Analysis -- Different Ways of Weakening Decision Trees and Their Impact on Classification Accuracy of DT Combination -- Applying Boosting to Similarity Literals for Time Series Classification -- Boosting of Tree-Based Classifiers for Predictive Risk Modeling in GIS -- A New Evaluation Method for Expert Combination in Multi-expert System Designing -- Diversity between Neural Networks and Decision Trees for Building Multiple Classifier Systems -- Self-Organizing Decomposition of Functions -- Classifier Instability and Partitioning -- A Hierarchical Multiclassifier System for Hyperspectral Data Analysis.-Consensus Based Classification of Multisource Remote Sensing Data -- Combining Parametric and Nonparametric Classifiers for an Unsupervised Updating of Land-Cover Maps -- A Multiple Self-Organizing Map Scheme for Remote Sensing Classification -- Use of Lexicon Density in Evaluating Word Recognizers -- A Multi-expert System for Dynamic Signature Verification -- A Cascaded Multiple Expert System for Verification -- Architecture for Classifier Combination Using Entropy Measures -- Combining Fingerprint Classifiers -- Statistical Sensor Calibration for Fusion of Different Classifiers in a Biometric Person Recognition Framework -- A Modular Neuro-Fuzzy Network for Musical Instruments Classification -- Classifier Combination for Grammar-Guided Sentence Recognition -- Shape Matching and Extraction by an Array of Figure-and-Ground Classifiers. 330 $aThis book constitutes the refereed proceedings of the First International Workshop on Multiple Classifier Systems, MCS 2000, held in Cagliari, Italy in June 2000.The 33 revised full papers presented together with five invited papers were carefully reviewed and selected for inclusion in the book. The papers are organized in topical sections on theoretical issues, multiple classifier fusion, bagging and boosting, design of multiple classifier systems, applications of multiple classifier systems, document analysis, and miscellaneous applications. 410 0$aLecture Notes in Computer Science,$x1611-3349 ;$v1857 606 $aPattern recognition systems 606 $aArtificial intelligence 606 $aComputer vision 606 $aAlgorithms 606 $aComputer science 606 $aAutomated Pattern Recognition 606 $aArtificial Intelligence 606 $aComputer Vision 606 $aAlgorithms 606 $aTheory of Computation 615 0$aPattern recognition systems. 615 0$aArtificial intelligence. 615 0$aComputer vision. 615 0$aAlgorithms. 615 0$aComputer science. 615 14$aAutomated Pattern Recognition. 615 24$aArtificial Intelligence. 615 24$aComputer Vision. 615 24$aAlgorithms. 615 24$aTheory of Computation. 676 $a006.3/1 702 $aKittler$b Josef$4edt$4http://id.loc.gov/vocabulary/relators/edt 702 $aRoli$b Fabio$4edt$4http://id.loc.gov/vocabulary/relators/edt 712 12$aInternational Workshop on Multiple Classifier Systems$d(1st :$f2000 :$eCagliari, Italy) 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910767550803321 996 $aMultiple Classifier Systems$9772217 997 $aUNINA