LEADER 05148nam 2200661Ia 450 001 9910484600003321 005 20200520144314.0 010 $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 $a20100329d2010 uy 0 101 0 $aeng 135 $aurnn#008mamaa 181 $ctxt 182 $cc 183 $acr 200 10$aMultiple classifier systems $e9th international workshop, MCS 2010, Cairo, Egypt, April 7-9, 2010 : proceedings /$fNeamat El Gayar, Josef Kittler, Fabio Roli (eds.) 205 $a1st ed. 2010. 210 $aBerlin $cSpringer$dc2010 215 $a1 online resource (X, 328 p. 77 illus.) 225 1 $aLecture notes in computer science,$x0302-9743 ;$v5997 225 1 $aLNCS sublibrary. SL 6, Image processing, computer vision, pattern recognition, and graphics 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$aLecture notes in computer science ;$v5997. 410 0$aLNCS sublibrary.$nSL 6,$pImage processing, computer vision, pattern recognition, and graphics. 517 3 $aMCS 2010 606 $aMachine learning$vCongresses 606 $aNeural networks (Computer science)$vCongresses 606 $aPattern perception$vCongresses 615 0$aMachine learning 615 0$aNeural networks (Computer science) 615 0$aPattern perception 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