000 05502nam a22006015i 4500
001 978-3-540-31578-0
003 DE-He213
005 20240730194747.0
007 cr nn 008mamaa
008 100714s2005 gw | s |||| 0|eng d
020 _a9783540315780
_9978-3-540-31578-0
024 7 _a10.1007/b136985
_2doi
050 4 _aQ337.5
050 4 _aTK7882.P3
072 7 _aUYQP
_2bicssc
072 7 _aCOM016000
_2bisacsh
072 7 _aUYQP
_2thema
082 0 4 _a006.4
_223
245 1 0 _aMultiple Classifier Systems
_h[electronic resource] :
_b6th International Workshop, MCS 2005, Seaside, CA, USA, June 13-15, 2005, Proceedings /
_cedited by Nikunj C. Oza, Robi Polikar, Josef Kittler, Fabio Roli.
250 _a1st ed. 2005.
264 1 _aBerlin, Heidelberg :
_bSpringer Berlin Heidelberg :
_bImprint: Springer,
_c2005.
300 _aXII, 432 p.
_bonline resource.
336 _atext
_btxt
_2rdacontent
337 _acomputer
_bc
_2rdamedia
338 _aonline resource
_bcr
_2rdacarrier
347 _atext file
_bPDF
_2rda
490 1 _aImage Processing, Computer Vision, Pattern Recognition, and Graphics,
_x3004-9954 ;
_v3541
505 0 _aFuture Directions -- Semi-supervised Multiple Classifier Systems: Background and Research Directions -- Boosting -- Boosting GMM and Its Two Applications -- Boosting Soft-Margin SVM with Feature Selection for Pedestrian Detection -- Observations on Boosting Feature Selection -- Boosting Multiple Classifiers Constructed by Hybrid Discriminant Analysis -- Combination Methods -- Decoding Rules for Error Correcting Output Code Ensembles -- A Probability Model for Combining Ranks -- EER of Fixed and Trainable Fusion Classifiers: A Theoretical Study with Application to Biometric Authentication Tasks -- Mixture of Gaussian Processes for Combining Multiple Modalities -- Dynamic Classifier Integration Method -- Recursive ECOC for Microarray Data Classification -- Using Dempster-Shafer Theory in MCF Systems to Reject Samples -- Multiple Classifier Fusion Performance in Networked Stochastic Vector Quantisers -- On Deriving the Second-Stage Training Set for Trainable Combiners -- Using Independence Assumption to Improve Multimodal Biometric Fusion -- Design Methods -- Half-Against-Half Multi-class Support Vector Machines -- Combining Feature Subsets in Feature Selection -- ACE: Adaptive Classifiers-Ensemble System for Concept-Drifting Environments -- Using Decision Tree Models and Diversity Measures in the Selection of Ensemble Classification Models -- Ensembles of Classifiers from Spatially Disjoint Data -- Optimising Two-Stage Recognition Systems -- Design of Multiple Classifier Systems for Time Series Data -- Ensemble Learning with Biased Classifiers: The Triskel Algorithm -- Cluster-Based Cumulative Ensembles -- Ensemble of SVMs for Incremental Learning -- Performance Analysis -- Design of a New Classifier Simulator -- Evaluation of Diversity Measures for Binary Classifier Ensembles -- Which Is the Best Multiclass SVM Method? An Empirical Study -- Over-Fitting in Ensembles of Neural Network Classifiers Within ECOC Frameworks -- Between Two Extremes: Examining Decompositions of the Ensemble Objective Function -- Data Partitioning Evaluation Measures for Classifier Ensembles -- Dynamics of Variance Reduction in Bagging and Other Techniques Based on Randomisation -- Ensemble Confidence Estimates Posterior Probability -- Applications -- Using Domain Knowledge in the Random Subspace Method: Application to the Classification of Biomedical Spectra -- An Abnormal ECG Beat Detection Approach for Long-Term Monitoring of Heart Patients Based on Hybrid Kernel Machine Ensemble -- Speaker Verification Using Adapted User-Dependent Multilevel Fusion -- Multi-modal Person Recognition for Vehicular Applications -- Using an Ensemble of Classifiers to Audit a Production Classifier -- Analysis and Modelling of Diversity Contribution to Ensemble-Based Texture Recognition Performance -- Combining Audio-Based and Video-Based Shot Classification Systems for News Videos Segmentation -- Designing Multiple Classifier Systems for Face Recognition -- Exploiting Class Hierarchies for Knowledge Transfer in Hyperspectral Data.
650 0 _aPattern recognition systems.
_93953
650 0 _aComputer vision.
_9157927
650 0 _aArtificial intelligence.
_93407
650 0 _aComputer science.
_99832
650 1 4 _aAutomated Pattern Recognition.
_931568
650 2 4 _aComputer Vision.
_9157928
650 2 4 _aArtificial Intelligence.
_93407
650 2 4 _aTheory of Computation.
_9157929
700 1 _aOza, Nikunj C.
_eeditor.
_4edt
_4http://id.loc.gov/vocabulary/relators/edt
_9157930
700 1 _aPolikar, Robi.
_eeditor.
_4edt
_4http://id.loc.gov/vocabulary/relators/edt
_9157931
700 1 _aKittler, Josef.
_eeditor.
_4edt
_4http://id.loc.gov/vocabulary/relators/edt
_9157932
700 1 _aRoli, Fabio.
_eeditor.
_4edt
_4http://id.loc.gov/vocabulary/relators/edt
_9157933
710 2 _aSpringerLink (Online service)
_9157934
773 0 _tSpringer Nature eBook
776 0 8 _iPrinted edition:
_z9783540263067
776 0 8 _iPrinted edition:
_z9783540812203
830 0 _aImage Processing, Computer Vision, Pattern Recognition, and Graphics,
_x3004-9954 ;
_v3541
_9157935
856 4 0 _uhttps://doi.org/10.1007/b136985
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