LEADER 05729nam 22008055 450 001 996465286003316 005 20230220150245.0 010 $a1-280-38597-9 010 $a9786613563897 010 $a3-642-12127-6 024 7 $a10.1007/978-3-642-12127-2 035 $a(CKB)2670000000010128 035 $a(SSID)ssj0000399491 035 $a(PQKBManifestationID)11290898 035 $a(PQKBTitleCode)TC0000399491 035 $a(PQKBWorkID)10385502 035 $a(PQKB)10646507 035 $a(DE-He213)978-3-642-12127-2 035 $a(MiAaPQ)EBC3065164 035 $a(PPN)149059876 035 $a(EXLCZ)992670000000010128 100 $a20100325d2010 u| 0 101 0 $aeng 135 $aurnn#008mamaa 181 $ctxt 182 $cc 183 $acr 200 10$aMultiple Classifier Systems$b[electronic resource] $e9th International Workshop, MCS 2010, Cairo, Egypt, April 7-9, 2010, Proceedings /$fedited by Neamat El Gayar, Josef Kittler, Fabio Roli 205 $a1st ed. 2010. 210 1$aBerlin, Heidelberg :$cSpringer Berlin Heidelberg :$cImprint: Springer,$d2010. 215 $a1 online resource (X, 328 p. 77 illus.) 225 1 $aTheoretical Computer Science and General Issues,$x2512-2029 ;$v5997 300 $aBibliographic Level Mode of Issuance: Monograph 311 $a3-642-12126-8 320 $aIncludes bibliographical references and index. 327 $aClassifier Ensembles(I) -- Weighted Bagging for Graph Based One-Class Classifiers -- Improving Multilabel Classification Performance by Using Ensemble of Multi-label Classifiers -- New Feature Splitting Criteria for Co-training Using Genetic Algorithm Optimization -- Incremental Learning of New Classes in Unbalanced Datasets: Learn?+?+?.UDNC -- Tomographic Considerations in Ensemble Bias/Variance Decomposition -- Choosing Parameters for Random Subspace Ensembles for fMRI Classification -- Classifier Ensembles(II) -- An Experimental Study on Ensembles of Functional Trees -- Multiple Classifier Systems under Attack -- SOCIAL: Self-Organizing ClassIfier ensemble for Adversarial Learning -- Unsupervised Change-Detection in Retinal Images by a Multiple-Classifier Approach -- A Double Pruning Algorithm for Classification Ensembles -- Estimation of the Number of Clusters Using Multiple Clustering Validity Indices -- Classifier Diversity -- ?Good? and ?Bad? Diversity in Majority Vote Ensembles -- Multi-information Ensemble Diversity -- Classifier Selection -- Dynamic Selection of Ensembles of Classifiers Using Contextual Information -- Selecting Structural Base Classifiers for Graph-Based Multiple Classifier Systems -- Combining Multiple Kernels -- A Support Kernel Machine for Supervised Selective Combining of Diverse Pattern-Recognition Modalities -- Combining Multiple Kernels by Augmenting the Kernel Matrix -- Boosting and Bootstrapping -- Class-Separability Weighting and Bootstrapping in Error Correcting Output Code Ensembles -- Boosted Geometry-Based Ensembles -- Online Non-stationary Boosting -- Handwriting Recognition -- Combining Neural Networks to Improve Performance of Handwritten Keyword Spotting -- Combining Committee-Based Semi-supervised and Active Learning and Its Application to Handwritten Digits Recognition -- Using Diversity in Classifier Set Selection for Arabic Handwritten Recognition -- Applications -- Forecast Combination Strategies for Handling Structural Breaks for Time Series Forecasting -- A Multiple Classifier System for Classification of LIDAR Remote Sensing Data Using Multi-class SVM -- A Multi-Classifier System for Off-Line Signature Verification Based on Dissimilarity Representation -- A Multi-objective Sequential Ensemble for Cluster Structure Analysis and Visualization and Application to Gene Expression -- Combining 2D and 3D Features to Classify Protein Mutants in HeLa Cells -- An Experimental Comparison of Hierarchical Bayes and True Path Rule Ensembles for Protein Function Prediction -- Recognizing Combinations of Facial Action Units with Different Intensity Using a Mixture of Hidden Markov Models and Neural Network -- Invited Papers -- Some Thoughts at the Interface of Ensemble Methods and Feature Selection -- Multiple Classifier Systems for the Recogonition of Human Emotions -- Erratum -- Erratum. 410 0$aTheoretical Computer Science and General Issues,$x2512-2029 ;$v5997 606 $aArtificial intelligence 606 $aApplication software 606 $aPattern recognition systems 606 $aAlgorithms 606 $aComputer science 606 $aDatabase management 606 $aArtificial Intelligence 606 $aComputer and Information Systems Applications 606 $aAutomated Pattern Recognition 606 $aAlgorithms 606 $aTheory of Computation 606 $aDatabase Management 615 0$aArtificial intelligence. 615 0$aApplication software. 615 0$aPattern recognition systems. 615 0$aAlgorithms. 615 0$aComputer science. 615 0$aDatabase management. 615 14$aArtificial Intelligence. 615 24$aComputer and Information Systems Applications. 615 24$aAutomated Pattern Recognition. 615 24$aAlgorithms. 615 24$aTheory of Computation. 615 24$aDatabase Management. 676 $a006.3 702 $aEl Gayar$b Neamat$4edt$4http://id.loc.gov/vocabulary/relators/edt 702 $aKittler$b Josef$4edt$4http://id.loc.gov/vocabulary/relators/edt 702 $aRoli$b Fabio$4edt$4http://id.loc.gov/vocabulary/relators/edt 712 12$aMCS 2010 906 $aBOOK 912 $a996465286003316 996 $aMultiple Classifier Systems$9772217 997 $aUNISA