Multiple Classifier Systems - Haindl, Michal / Kittler, Josef / Roli, Fabio (eds.)
79,99 €
versandkostenfrei*

inkl. MwSt.
Versandfertig in 2-4 Wochen
40 °P sammeln
  • Broschiertes Buch

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. It covers 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. …mehr

Produktbeschreibung
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. It covers 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.
  • Produktdetails
  • Lecture Notes in Computer Science Vol.4472
  • Verlag: Springer, Berlin
  • Artikelnr. des Verlages: 12062539
  • 2007
  • Seitenzahl: 540
  • Erscheinungstermin: 9. Mai 2007
  • Englisch
  • Abmessung: 235mm x 155mm x 28mm
  • Gewicht: 807g
  • ISBN-13: 9783540724810
  • ISBN-10: 3540724818
  • Artikelnr.: 22817825
Autorenporträt
Michal Haindl, Academy of Sciences of the Czech Republic, Prague, Czech Republic / Josef Kittler, University of Surrey, Guildford, UK / Fabio Roli, University of Cagliari, Italy
Inhaltsangabe
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 in One-Against-Other Classifiers.- Random Feature Subset Selection for Ensemble Based Classification of Data with Missing Features.- Feature Subspace Ensembles: A Parallel Classifier Combination Scheme