LEADER 07437nam 22007575 450 001 9910144153303321 005 20200702161230.0 010 $a1-280-30778-1 010 $a9786610307784 010 $a3-540-25966-X 024 7 $a10.1007/b98227 035 $a(CKB)1000000000212430 035 $a(DE-He213)978-3-540-25966-4 035 $a(SSID)ssj0000206703 035 $a(PQKBManifestationID)11184063 035 $a(PQKBTitleCode)TC0000206703 035 $a(PQKBWorkID)10246496 035 $a(PQKB)10413290 035 $a(MiAaPQ)EBC3088948 035 $a(PPN)155176250 035 $a(EXLCZ)991000000000212430 100 $a20121227d2004 u| 0 101 0 $aeng 135 $aurnn#008mamaa 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aMultiple Classifier Systems $e5th International Workshop, MCS 2004, Cagliari, Italy, June 9-11, 2004, Proceedings /$fedited by Fabio Roli, Josef Kittler, Terry Windeatt 205 $a1st ed. 2004. 210 1$aBerlin, Heidelberg :$cSpringer Berlin Heidelberg :$cImprint: Springer,$d2004. 215 $a1 online resource (XII, 392 p.) 225 1 $aLecture Notes in Computer Science,$x0302-9743 ;$v3077 300 $aBibliographic Level Mode of Issuance: Monograph 311 $a3-540-22144-1 320 $aIncludes bibliographical references at the end of each chapters and index. 327 $aInvited Papers -- Classifier Ensembles for Changing Environments -- A Generic Sensor Fusion Problem: Classification and Function Estimation -- Bagging and Boosting -- AveBoost2: Boosting for Noisy Data -- Bagging Decision Multi-trees -- Learn++.MT: A New Approach to Incremental Learning -- Beyond Boosting: Recursive ECOC Learning Machines -- Exact Bagging with k-Nearest Neighbour Classifiers -- Combination Methods -- Yet Another Method for Combining Classifiers Outputs: A Maximum Entropy Approach -- Combining One-Class Classifiers to Classify Missing Data -- Combining Kernel Information for Support Vector Classification -- Combining Classifiers Using Dependency-Based Product Approximation with Bayes Error Rate -- Combining Dissimilarity-Based One-Class Classifiers -- A Modular System for the Classification of Time Series Data -- A Probabilistic Model Using Information Theoretic Measures for Cluster Ensembles -- Classifier Fusion Using Triangular Norms -- Dynamic Integration of Regression Models -- Dynamic Classifier Selection by Adaptive k-Nearest-Neighbourhood Rule -- Design Methods -- Spectral Measure for Multi-class Problems -- The Relationship between Classifier Factorisation and Performance in Stochastic Vector Quantisation -- A Method for Designing Cost-Sensitive ECOC -- Building Graph-Based Classifier Ensembles by Random Node Selection -- A Comparison of Ensemble Creation Techniques -- Multiple Classifiers System for Reducing Influences of Atypical Observations -- Sharing Training Patterns among Multiple Classifiers -- Performance Analysis -- First Experiments on Ensembles of Radial Basis Functions -- Random Aggregated and Bagged Ensembles of SVMs: An Empirical Bias?Variance Analysis -- Building Diverse Classifier Outputs to Evaluate the Behavior of Combination Methods: The Case of Two Classifiers -- An Empirical Comparison of Hierarchical vs. Two-Level Approaches to Multiclass Problems -- Experiments on Ensembles with Missing and Noisy Data -- Applications -- Induced Decision Fusion in Automated Sign Language Interpretation: Using ICA to Isolate the Underlying Components of Sign -- Ensembles of Classifiers Derived from Multiple Prototypes and Their Application to Handwriting Recognition -- Network Intrusion Detection by a Multi-stage Classification System -- Application of Breiman?s Random Forest to Modeling Structure-Activity Relationships of Pharmaceutical Molecules -- Experimental Study on Multiple LDA Classifier Combination for High Dimensional Data Classification -- Physics-Based Decorrelation of Image Data for Decision Level Fusion in Face Verification -- High Security Fingerprint Verification by Perceptron-Based Fusion of Multiple Matchers -- Second Guessing a Commercial?Black Box? Classifier by an?In House? Classifier: Serial Classifier Combination in a Speech Recognition Application. 330 $aThe fusion of di?erent information sourcesis a persistent and intriguing issue. It hasbeenaddressedforcenturiesinvariousdisciplines,includingpoliticalscience, probability and statistics, system reliability assessment, computer science, and distributed detection in communications. Early seminal work on fusion was c- ried out by pioneers such as Laplace and von Neumann. More recently, research activities in information fusion have focused on pattern recognition. During the 1990s,classi?erfusionschemes,especiallyattheso-calleddecision-level,emerged under a plethora of di?erent names in various scienti?c communities, including machine learning, neural networks, pattern recognition, and statistics. The d- ferent nomenclatures introduced by these communities re?ected their di?erent perspectives and cultural backgrounds as well as the absence of common forums and the poor dissemination of the most important results. In 1999, the ?rst workshop on multiple classi?er systems was organized with the main goal of creating a common international forum to promote the diss- ination of the results achieved in the diverse communities and the adoption of a common terminology, thus giving the di?erent perspectives and cultural ba- grounds some concrete added value. After ?ve meetings of this workshop, there is strong evidence that signi?cant steps have been made towards this goal. - searchers from these diverse communities successfully participated in the wo- shops, and world experts presented surveys of the state of the art from the perspectives of their communities to aid cross-fertilization. 410 0$aLecture Notes in Computer Science,$x0302-9743 ;$v3077 606 $aArtificial intelligence 606 $aPattern recognition 606 $aOptical data processing 606 $aComputers 606 $aArtificial Intelligence$3https://scigraph.springernature.com/ontologies/product-market-codes/I21000 606 $aPattern Recognition$3https://scigraph.springernature.com/ontologies/product-market-codes/I2203X 606 $aImage Processing and Computer Vision$3https://scigraph.springernature.com/ontologies/product-market-codes/I22021 606 $aComputation by Abstract Devices$3https://scigraph.springernature.com/ontologies/product-market-codes/I16013 615 0$aArtificial intelligence. 615 0$aPattern recognition. 615 0$aOptical data processing. 615 0$aComputers. 615 14$aArtificial Intelligence. 615 24$aPattern Recognition. 615 24$aImage Processing and Computer Vision. 615 24$aComputation by Abstract Devices. 676 $a006.31 702 $aRoli$b Fabio$4edt$4http://id.loc.gov/vocabulary/relators/edt 702 $aKittler$b Josef$4edt$4http://id.loc.gov/vocabulary/relators/edt 702 $aWindeatt$b Terry$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 $a9910144153303321 996 $aMultiple Classifier Systems$9772217 997 $aUNINA