LEADER 07465nam 22008655 450 001 9910484600003321 005 20251226202304.0 010 $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(BIP)29190111 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 $e9th International Workshop, MCS 2010, Cairo, Egypt, April 7-9, 2010, Proceedings /$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 $aBibliographic Level Mode of Issuance: Monograph 311 08$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 toHandwritten 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. 330 $aThese proceedings are a record of the Multiple Classi'er Systems Workshop, MCS 2010, held at the Nile University, Egypt in April 2010. Being the ninth in a well-established series of meetings providing an international forum for d- cussion of issues in multiple classi'er system design, the workshop achieved its objective of bringing together researchers from diverse communities (neural n- works, pattern recognition, machine learning and statistics) concerned with this researchtopic.Frommorethan50submissions,theProgramCommitteeselected 31 papers to create an interesting scienti'c program.Paperswere organizedinto sessionsdealingwithclassi'ercombinationandclassi'erselection,diversity,b- ging and boosting, combination of multiple kernels, and applications. The wo- shopprogramandthisvolumewereenrichedbytwoinvitedtalksgivenbyGavin Brown(University of Manchester,UK), and Friedhelm Schwenker(University of Ulm, Germany). As usual, the workshop would not have been possible without the help of many individuals and organizations. First of all, our thanks go to the members of the MCS 2010 Program Committee, whose expertise and dedication helped us create an interesting event that marks the progressmade in this ?eld overthe last year and aspire to chart its future research. The help of James Field from the University of Surrey, who administered the submitted paper reviews, and of Giorgio Fumera who managed the MCS website deserve a particular mention. Special thanks are due to the members of the Nile University Organizing C- mittee,AhmedSalah,AmiraElBaroudy,EsraaAly,HebaEzzat,NesrineSameh, Rana Salah and Mohamed Zahhar for their indispensable contributions to the registration management, local organization, and proceedings preparation. 410 0$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 $a006.3 701 $aEl Gayar$b Neamat$01754896 701 $aKittler$b Josef$f1946-$013183 701 $aRoli$b Fabio$f1962-$0275187 712 12$aMCS 2010 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910484600003321 996 $aMultiple classifier systems$94191409 997 $aUNINA