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) - edizione con copertina flessibile

EAN (ISBN-13): 9783540724810
ISBN (ISBN-10): 3540724818
Copertina rigida
Copertina flessibile
Anno di pubblicazione: 2007
Editore: Springer Berlin
524 Pagine
Peso: 0,813 kg
Lingua: eng/Englisch

Libro nella banca dati dal 2007-06-04T22:27:20+02:00 (Zurich)
Pagina di dettaglio ultima modifica in 2023-03-18T19:28:47+01:00 (Zurich)
ISBN/EAN: 9783540724810

ISBN - Stili di scrittura alternativi:
3-540-72481-8, 978-3-540-72481-0
Stili di scrittura alternativi e concetti di ricerca simili:
Autore del libro : haindl, kittler, fabio, kittl, michal, six josef, unknown
Titolo del libro: 2007, proceedings international workshop, czech republic, czech vision, prague, multiple, mcs, international graphics, computer vision graphics, work, 7th international

---

Dati dell'editore

Autore: Michal Haindl; Josef Kittler; Fabio Roli
Titolo: 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
Editore: Springer; Springer Berlin
524 Pagine
Anno di pubblicazione: 2007-05-09
Berlin; Heidelberg; DE
Lingua: Inglese
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.