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
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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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