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Haindl, Michal [Editor]; Kittler, Josef [Editor]; Roli, Fabio [Editor];:

Multiple Classifier Systems: 7th International Workshop, MCS 2007, Prague, Czech Republic, May 23-25, 2007, Proceedings (Lecture Notes in Computer ... Vision, Pattern Recognition, and Graphics) - Taschenbuch

2007, ISBN: 9783540724810

Springer, 2007-05-09. Paperback. Very Good. Ex-library paperback in very nice condition with the usual markings and attachments. Text block clean and unmarked. Tight binding., Springer,… Mehr…

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michal (editor) ; kittler, josef (editor) ; roli, fabio (editor) haindl:

Multiple Classifier Systems: 7th International Workshop, MCS 2007, Prague, Czech Republic, May 23-25, 2007, Proceedings (Lecture Notes in Computer ... Vision, Pattern Recognition, and Graphics) - Taschenbuch

2007, ISBN: 9783540724810

Springer, 2007-06-12. 2007. Paperback. Used:Good., Springer, 2007-06-12, 0

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Editor-Michal Haindl; Editor-Josef Kittler; Editor-Fabio Roli:
Multiple Classifier Systems: 7th International Workshop, MCS 2007, Prague, Czech Republic, May 23-25, 2007, Proceedings (Lecture Notes in Computer ... Vision, Pattern Recognition, and Graphics) - Taschenbuch

2007

ISBN: 9783540724810

Springer, 2007-06-12. Paperback. Used: Good., Springer, 2007-06-12, 2.5

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Josef Kittler, Fabio Roli, Michal Haindl:
Multiple Classifier Systems: 7Th International Workshop, Mcs 2007, Prague, Czech Republic, May 23-25, 2007, Proceedings - neues Buch

2007, ISBN: 9783540724810

New/New. Brand New Original US Edition, Perfect Condition. Printed in English. Excellent Quality, Service and customer satisfaction guaranteed!, 6

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Michal Haindl; Josef Kittler; Fabio Roli:
Multiple Classifier Systems - Erstausgabe

2007, ISBN: 9783540724810

Taschenbuch

7th International Workshop, MCS 2007, Prague, Czech Republic, May 23-25, 2007, Proceedings, Buch, Softcover, [PU: Springer Berlin], [ED: 1], Springer Berlin, 2007

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Details zum Buch
Multiple Classifier Systems

This book constitutes the refereed proceedings of the 7th International Workshop on Multiple Classifier Systems, MCS 2007, held in Prague, Czech Republic in May 2007. The 49 revised full papers presented together with 2 invited papers were carefully reviewed and selected from more than 80 initial submissions. The papers are organized in topical sections on kernel-based fusion, applications, boosting, cluster and graph ensembles, feature subspace ensembles, multiple classifier system theory, intramodal and multimodal fusion of biometric experts, majority voting, and ensemble learning.

Detailangaben zum Buch - Multiple Classifier Systems


EAN (ISBN-13): 9783540724810
ISBN (ISBN-10): 3540724818
Gebundene Ausgabe
Taschenbuch
Erscheinungsjahr: 2007
Herausgeber: Springer Berlin
524 Seiten
Gewicht: 0,813 kg
Sprache: eng/Englisch

Buch in der Datenbank seit 2007-06-04T22:27:20+02:00 (Berlin)
Detailseite zuletzt geändert am 2023-03-18T19:28:47+01:00 (Berlin)
ISBN/EAN: 9783540724810

ISBN - alternative Schreibweisen:
3-540-72481-8, 978-3-540-72481-0
Alternative Schreibweisen und verwandte Suchbegriffe:
Autor des Buches: haindl, kittler, fabio, kittl, michal, six josef, unknown
Titel des Buches: 2007, proceedings international workshop, czech republic, czech vision, prague, multiple, mcs, international graphics, computer vision graphics, work, 7th international


Daten vom Verlag:

Autor/in: Michal Haindl; Josef Kittler; Fabio Roli
Titel: Lecture Notes in Computer Science; Image Processing, Computer Vision, Pattern Recognition, and Graphics; Multiple Classifier Systems - 7th International Workshop, MCS 2007, Prague, Czech Republic, May 23-25, 2007, Proceedings
Verlag: Springer; Springer Berlin
524 Seiten
Erscheinungsjahr: 2007-05-09
Berlin; Heidelberg; DE
Sprache: Englisch
53,49 € (DE)
54,99 € (AT)
59,00 CHF (CH)
Available
XI, 524 p.

BC; Hardcover, Softcover / Informatik, EDV/Anwendungs-Software; Mustererkennung; Verstehen; Bayesian network; Performance; Textur; algorithmic learning; bayesian networks; classification; cognition; decision trees; document analysis; ensemble prediction; genetic networks; learning classifier; networks; systems theory; verification; Automated Pattern Recognition; Computer Vision; Artificial Intelligence; Biometrics; Theory of Computation; Maschinelles Sehen, Bildverstehen; Künstliche Intelligenz; Theoretische Informatik; EA

Kernel-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 inOne-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 GeneExpression 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.

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