000			05392nam a22005895i 4500
001			978-3-642-12127-2
003			DE-He213
005			20140220084533.0
007			cr nn 008mamaa
008			100325s2010 gw | s |||| 0|eng d
020			_a9783642121272 _9978-3-642-12127-2
024	7		_a10.1007/978-3-642-12127-2 _2doi
050		4	_aQA76.76.A65
072		7	_aUNH _2bicssc
072		7	_aUDBD _2bicssc
072		7	_aCOM032000 _2bisacsh
082	0	4	_a005.7 _223
100	1		_aGayar, Neamat. _eeditor.
245	1	0	_aMultiple Classifier Systems _h[electronic resource] : _b9th International Workshop, MCS 2010, Cairo, Egypt, April 7-9, 2010. Proceedings / _cedited by Neamat Gayar, Josef Kittler, Fabio Roli.
264		1	_aBerlin, Heidelberg : _bSpringer Berlin Heidelberg, _c2010.
300			_aX, 328p. 77 illus. _bonline resource.
336			_atext _btxt _2rdacontent
337			_acomputer _bc _2rdamedia
338			_aonline resource _bcr _2rdacarrier
347			_atext file _bPDF _2rda
490	1		_aLecture Notes in Computer Science, _x0302-9743 ; _v5997
505	0		_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.
520			_aThis book constitutes the proceedings of the 9th International Workshop on Multiple Classifier Systems, MCS 2010, held in Cairo, Egypt, in April 2010. The 31 papers presented were carefully reviewed and selected from 50 submissions. The contributions are organized into sessions dealing with classifier combination and classifier selection, diversity, bagging and boosting, combination of multiple kernels, and applications.
650		0	_aComputer science.
650		0	_aComputer software.
650		0	_aDatabase management.
650		0	_aData mining.
650		0	_aInformation systems.
650		0	_aOptical pattern recognition.
650	1	4	_aComputer Science.
650	2	4	_aInformation Systems Applications (incl.Internet).
650	2	4	_aPattern Recognition.
650	2	4	_aAlgorithm Analysis and Problem Complexity.
650	2	4	_aComputation by Abstract Devices.
650	2	4	_aDatabase Management.
650	2	4	_aData Mining and Knowledge Discovery.
700	1		_aKittler, Josef. _eeditor.
700	1		_aRoli, Fabio. _eeditor.
710	2		_aSpringerLink (Online service)
773	0		_tSpringer eBooks
776	0	8	_iPrinted edition: _z9783642121265
830		0	_aLecture Notes in Computer Science, _x0302-9743 ; _v5997
856	4	0	_uhttp://dx.doi.org/10.1007/978-3-642-12127-2
912			_aZDB-2-SCS
912			_aZDB-2-LNC
999			_c111999 _d111999