Kategorien
Konto
Anmelden / Registrieren
Warenkorb
 
 

Multiple Classifier Systems


Menge:  Stück  
Produktinformationen
cover
cover
Artikel-Nr.:
     858A-9783540263067
Hersteller:
     Springer Verlag
Herst.-Nr.:
     9783540263067
EAN/GTIN:
     9783540263067
Suchbegriffe:
Allgemeine Informatikbücher
Bücher für Datenbanken - englischsp...
Datenbanken (Fachbücher)
Datenbankenbücher
Future 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.
Weitere Informationen:
Author:
Nikunj C. Oza; Robi Polikar; Josef Kittler; Fabio Roli
Verlag:
Springer Berlin
Sprache:
eng
Weitere Suchbegriffe: allgemeine Informatikbücher - englischsprachig, allgemeine informatikbücher - englischsprachig, Klassifikation, Performance, Textur, algorithmic learning, algorithms, biometric authentication, classification, classifier systems, clustering, cognition, document analysis
Die Konditionen im Überblick1
Lieferzeit
Lagerstand
Preis
€ 49,99*
Konditionen selbst auswählen
Artikel empfehlenArtikel merken
* Preise mit Sternchen sind Nettopreise zzgl. gesetzlich gültiger MwSt.
UVP bedeutet „Unverbindliche Preisempfehlung“
Unser Angebot richtet sich ausschließlich an Unternehmen, Gewerbetreibende und Freiberufler.