LEADER 07145nam 2200637 450 001 996466261203316 005 20220513171901.0 010 $a1-280-94918-X 010 $a9786610949182 010 $a3-540-72523-7 024 7 $a10.1007/978-3-540-72523-7 035 $a(CKB)1000000000478514 035 $a(EBL)3037354 035 $a(SSID)ssj0000206704 035 $a(PQKBManifestationID)11954559 035 $a(PQKBTitleCode)TC0000206704 035 $a(PQKBWorkID)10228991 035 $a(PQKB)11011777 035 $a(DE-He213)978-3-540-72523-7 035 $a(MiAaPQ)EBC3037354 035 $a(MiAaPQ)EBC6711212 035 $a(Au-PeEL)EBL6711212 035 $a(OCoLC)184986059 035 $a(PPN)123162300 035 $a(EXLCZ)991000000000478514 100 $a20220513d2007 uy 0 101 0 $aeng 135 $aur|n|---||||| 181 $ctxt 182 $cc 183 $acr 200 00$aMultiple classifier systems $e7th international workshop, MCS 2007, Prague, Czech Republic, May 23-25, 2007 : proceedings /$fMichal Haindl, Josef Kittler, Fabio Roli (editors) 205 $a1st ed. 2007. 210 1$aBerlin ;$aHeidelberg ;$aNew York :$cSpringer,$d[2007] 210 4$d©2007 215 $a1 online resource (534 p.) 225 1 $aLecture notes in computer science ;$v4472 300 $aDescription based upon print version of record. 311 $a3-540-72481-8 320 $aIncludes bibliographical references and index. 327 $aKernel-Based Fusion -- Combining Pattern Recognition Modalities at the Sensor Level Via Kernel Fusion -- The Neutral Point Method for Kernel-Based Combination of Disjoint Training Data in Multi-modal Pattern Recognition -- Kernel Combination Versus Classifier Combination -- Deriving the Kernel from Training Data -- Applications -- On the Application of SVM-Ensembles Based on Adapted Random Subspace Sampling for Automatic Classification of NMR Data -- A New HMM-Based Ensemble Generation Method for Numeral Recognition -- Classifiers Fusion in Recognition of Wheat Varieties -- Multiple Classifier Methods for Offline Handwritten Text Line Recognition -- Applying Data Fusion Methods to Passage Retrieval in QAS -- A Co-training Approach for Time Series Prediction with Missing Data -- An Improved Random Subspace Method and Its Application to EEG Signal Classification -- Ensemble Learning Methods for Classifying EEG Signals -- Confidence Based Gating of Colour Features for Face Authentication -- View-Based Eigenspaces with Mixture of Experts for View-Independent Face Recognition -- Fusion of Support Vector Classifiers for Parallel Gabor Methods Applied to Face Verification -- Serial Fusion of Fingerprint and Face Matchers -- Boosting -- Boosting Lite ? Handling Larger Datasets and Slower Base Classifiers -- Information Theoretic Combination of Classifiers with Application to AdaBoost -- Interactive Boosting for Image Classification -- Cluster and Graph Ensembles -- Group-Induced Vector Spaces -- Selecting Diversifying Heuristics for Cluster Ensembles -- Unsupervised Texture Segmentation Using Multiple Segmenters Strategy -- Classifier Ensembles for Vector Space Embedding of Graphs -- Cascading for Nominal Data -- Feature Subspace Ensembles -- A Combination of Sample Subsets and Feature Subsets in One-Against-Other Classifiers -- Random Feature Subset Selection for Ensemble Based Classification of Data with Missing Features -- Feature Subspace Ensembles: A Parallel Classifier Combination Scheme Using Feature Selection -- Stopping Criteria for Ensemble-Based Feature Selection -- Multiple Classifier System Theory -- On Rejecting Unreliably Classified Patterns -- Bayesian Analysis of Linear Combiners -- Applying Pairwise Fusion Matrix on Fusion Functions for Classifier Combination -- Modelling Multiple-Classifier Relationships Using Bayesian Belief Networks -- Classifier Combining Rules Under Independence Assumptions -- Embedding Reject Option in ECOC Through LDPC Codes -- Intramodal and Multimodal Fusion of Biometric Experts -- On Combination of Face Authentication Experts by a Mixture of Quality Dependent Fusion Classifiers -- Index Driven Combination of Multiple Biometric Experts for AUC Maximisation -- Q???stack: Uni- and Multimodal Classifier Stacking with Quality Measures -- Reliability-Based Voting Schemes Using Modality-Independent Features in Multi-classifier Biometric Authentication -- Optimal Classifier Combination Rules for Verification and Identification Systems -- Majority Voting -- Exploiting Diversity in Ensembles: Improving the Performance on Unbalanced Datasets -- On the Diversity-Performance Relationship for Majority Voting in Classifier Ensembles -- Hierarchical Behavior Knowledge Space -- Ensemble Learning -- A New Dynamic Ensemble Selection Method for Numeral Recognition -- Ensemble Learning in Linearly Combined Classifiers Via Negative Correlation -- Naïve Bayes Ensembles with a Random Oracle -- An Experimental Study on Rotation Forest Ensembles -- Cooperative Coevolutionary Ensemble Learning -- Robust Inference in Bayesian Networks with Application to Gene Expression Temporal Data -- An Ensemble Approach for Incremental Learning in Nonstationary Environments -- Invited Papers -- Multiple Classifier Systems in Remote Sensing: From Basics to Recent Developments -- Biometric Person Authentication Is a Multiple Classifier Problem. 330 $aThese proceedings are a record of the Multiple Classi?er Systems Workshop, MCS 2007, held at the Institute of Information Theory and Automation, Czech Academy of Sciences, Prague in May 2007. Being the seventh in a well-established series of meetings providing an international forum for the discussion of issues in multiple classi?er system design, the workshop achieved its objective of bringing together researchers from diverse communities (neural networks, pattern rec- nition, machine learning and statistics) concerned with this research topic. From more than 80 submissions, the Programme Committee selected 49 - pers to create an interesting scienti?c programme. The special focus of MCS 2007 was on the application of multiple classi?er systems in biometrics. This part- ular application area exercises all aspects of multiple classi?er fusion, from - tramodal classi?er combination, through con?dence-based fusion, to multimodal biometric systems. The sponsorship of MCS 2007 by the European Union N- work of Excellence in Biometrics BioSecure and in Multimedia Understanding through Semantics, Computation and Learning MUSCLE and their assistance in selecting the contributions to the MCS 2007 programme consistent with this theme is gratefully acknowledged. 410 0$aLecture notes in computer science ;$v4472. 606 $aMachine learning$vCongresses 615 0$aMachine learning 676 $a006.31 702 $aHaindl$b Michal 702 $aKittler$b Josef$f1946- 702 $aRoli$b Fabio$f1962- 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a996466261203316 996 $aMultiple Classifier Systems$9772217 997 $aUNISA LEADER 01873nam 2200445 450 001 9910808355803321 005 20230803214741.0 010 $a2-8062-6046-9 035 $a(CKB)3790000000017918 035 $a(EBL)2072819 035 $a(OCoLC)912233724 035 $a(MiAaPQ)EBC2072819 035 $a(Au-PeEL)EBL2072819 035 $a(EXLCZ)993790000000017918 100 $a20200122d2014 uy 0 101 0 $afre 135 $aur|n|---||||| 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 03$aUn corps sublime$hNumber 11 /$fre?daction, Maud Gaudin ; illustrations, Julie Oldenhove 210 1$aNamur :$cLemaitre Publishing,$d[2014] 210 4$d©2014 215 $a1 online resource (37 p.) 225 0 $a25 trucs et astuces de grand-me?re 300 $aDescription based upon print version of record. 311 $a2-8062-6047-7 330 $a Le guide pratique pour entretenir harmonieusement votre corpsCe livre s'adresse a? vous, les insatisfaites chroniques des produits cosme?tiques achete?s dans les commerces traditionnels ! 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Commencez de?s a? pre?sent a? travailler votre silhouette et a? prendre soin de votre organisme a? l'aide de ces astuces se?culaires !Pourquoi acheter ce livre ? 25 astuces faciles a? appliquer chez soi Trucs inde?modables pour vous faciliter la vie Ingre?dien 606 $aMeditation 615 0$aMeditation. 676 $a158.12 702 $aGaudin$b Maud 702 $aOldenhove$b Julie 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910808355803321 996 $aUn corps sublime$94053742 997 $aUNINA LEADER 01657nas 2200457-a 450 001 9910132123703321 005 20240413023116.0 035 $a(CKB)110978979118553 035 $a(CONSER)sn-86010097- 035 $a(DE-599)ZDB2401296-8 035 $a(EXLCZ)99110978979118553 100 $a19860702a19859999 -b- a 101 0 $aeng 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 00$aNew German review 210 $aLos Angeles, Calif. $cUniversity of California, Los Angeles, in connection with the Dept. of Germanic Languages$d©1985- 215 $a1 online resource 300 $aRefereed/Peer-reviewed 311 08$aPrint version: New German review. 0889-0145 (DLC)sn 86010097 (OCoLC)13812374 517 1 $aNGR 531 $aNEW GERMAN REVIEW A JOURNAL OF GERMANIC STUDIES 531 0 $aNew Ger. rev. 606 $aGerman literature$xHistory and criticism$vPeriodicals 606 $aGermanic literature$xHistory and criticism$vPeriodicals 606 $aGerman literature$2fast$3(OCoLC)fst00941797 606 $aGermanic literature$2fast$3(OCoLC)fst00942045 608 $aCriticism, interpretation, etc.$2fast 608 $aPeriodicals.$2fast 615 0$aGerman literature$xHistory and criticism 615 0$aGermanic literature$xHistory and criticism 615 7$aGerman literature. 615 7$aGermanic literature. 676 $a830 712 02$aUniversity of California, Los Angeles.$bDepartment of Germanic Languages. 906 $aJOURNAL 912 $a9910132123703321 920 $aexl_impl conversion 996 $aNew German review$92275754 997 $aUNINA