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008			150903s2005 gw | o |||| 0|eng d
020			_a9783540315780 _99783540315780
024	7		_a10.1007/b136985 _2doi
035			_avtls000347795
039		9	_a201509030437 _bVLOAD _c201405070505 _dVLOAD _y201402070939 _zstaff
040			_aMX-SnUAN _bspa _cMX-SnUAN _erda
050		4	_aQ337.5
100	1		_aOza, Nikunj C. _eeditor. _9328728
245	1	0	_aMultiple Classifier Systems : _b6th International Workshop, MCS 2005, Seaside, CA, USA, June 13-15, 2005. Proceedings / _cedited by Nikunj C. Oza, Robi Polikar, Josef Kittler, Fabio Roli.
264		1	_aBerlin, Heidelberg : _bSpringer Berlin Heidelberg, _c2005.
300			_axii, 430 páginas Also available online. _brecurso en línea.
336			_atexto _btxt _2rdacontent
337			_acomputadora _bc _2rdamedia
338			_arecurso en línea _bcr _2rdacarrier
347			_aarchivo de texto _bPDF _2rda
490	0		_aLecture Notes in Computer Science, _x0302-9743 ; _v3541
500			_aSpringer eBooks
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.
520			_aThis book constitutes the refereed proceedings of the 6th International Workshop on Multiple Classifier Systems, MCS 2005, held in Seaside, CA, USA in June 2005. The 42 revised full papers presented were carefully reviewed and are organized in topical sections on boosting, combination methods, design of ensembles, performance analysis, and applications. They exemplify significant advances in the theory, algorithms, and applications of multiple classifier systems – bringing the different scientific communities together.
590			_aPara consulta fuera de la UANL se requiere clave de acceso remoto.
700	1		_aPolikar, Robi. _eeditor. _9328729
700	1		_aKittler, Josef. _eeditor. _9328730
700	1		_aRoli, Fabio. _eeditor. _9328731
710	2		_aSpringerLink (Servicio en línea) _9299170
776	0	8	_iEdición impresa: _z9783540263067
856	4	0	_uhttp://remoto.dgb.uanl.mx/login?url=http://dx.doi.org/10.1007/b136985 _zConectar a Springer E-Books (Para consulta externa se requiere previa autentificación en Biblioteca Digital UANL)
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999			_c294816 _d294816