Vor Ort
Merkzettel Warenkorb
  • Produktbild: Multiple Classifier Systems
  • Produktbild: Multiple Classifier Systems
Band 3077

Multiple Classifier Systems 5th International Workshop, MCS 2004, Cagliari, Italy, June 9-11, 2004, Proceedings

Aus der Reihe Lecture Notes in Computer Science

51,99 €

inkl. gesetzl. MwSt., Versandkostenfrei

Beschreibung

Produktdetails

Einband

Taschenbuch

Erscheinungsdatum

01.06.2004

Herausgeber

Fabio Roli + weitere

Verlag

Springer Berlin

Seitenzahl

392

Maße (L/B/H)

23,5/15,5/2,2 cm

Gewicht

600 g

Auflage

2004

Sprache

Englisch

ISBN

978-3-540-22144-9

Beschreibung

Produktdetails

Einband

Taschenbuch

Erscheinungsdatum

01.06.2004

Herausgeber

Verlag

Springer Berlin

Seitenzahl

392

Maße (L/B/H)

23,5/15,5/2,2 cm

Gewicht

600 g

Auflage

2004

Sprache

Englisch

ISBN

978-3-540-22144-9

Herstelleradresse

Springer-Verlag KG
Sachsenplatz 4-6
1201 Wien
AT

Email: GPSR Kontakt

Noch keine Bewertungen vorhanden

Verfassen Sie die erste Bewertung zu diesem Artikel

Helfen Sie anderen Kundinnen und Kunden durch Ihre Meinung.

Kundinnen und Kunden meinen

Bewertungen (0)
  • Produktbild: Multiple Classifier Systems
  • Produktbild: Multiple Classifier Systems
  • Invited Papers.- Classifier Ensembles for Changing Environments.- A Generic Sensor Fusion Problem: Classification and Function Estimation.- Bagging and Boosting.- AveBoost2: Boosting for Noisy Data.- Bagging Decision Multi-trees.- Learn++.MT: A New Approach to Incremental Learning.- Beyond Boosting: Recursive ECOC Learning Machines.- Exact Bagging with k-Nearest Neighbour Classifiers.- Combination Methods.- Yet Another Method for Combining Classifiers Outputs: A Maximum Entropy Approach.- Combining One-Class Classifiers to Classify Missing Data.- Combining Kernel Information for Support Vector Classification.- Combining Classifiers Using Dependency-Based Product Approximation with Bayes Error Rate.- Combining Dissimilarity-Based One-Class Classifiers.- A Modular System for the Classification of Time Series Data.- A Probabilistic Model Using Information Theoretic Measures for Cluster Ensembles.- Classifier Fusion Using Triangular Norms.- Dynamic Integration of Regression Models.- Dynamic Classifier Selection by Adaptive k-Nearest-Neighbourhood Rule.- Design Methods.- Spectral Measure for Multi-class Problems.- The Relationship between Classifier Factorisation and Performance in Stochastic Vector Quantisation.- A Method for Designing Cost-Sensitive ECOC.- Building Graph-Based Classifier Ensembles by Random Node Selection.- A Comparison of Ensemble Creation Techniques.- Multiple Classifiers System for Reducing Influences of Atypical Observations.- Sharing Training Patterns among Multiple Classifiers.- Performance Analysis.- First Experiments on Ensembles of Radial Basis Functions.- Random Aggregated and Bagged Ensembles of SVMs: An Empirical Bias–Variance Analysis.- Building Diverse Classifier Outputs to Evaluate the Behavior of Combination Methods: The Case of Two Classifiers.- An Empirical Comparison of Hierarchical vs. Two-Level Approaches to Multiclass Problems.- Experiments on Ensembles with Missing and Noisy Data.- Applications.- Induced Decision Fusion in Automated Sign Language Interpretation: Using ICA to Isolate the Underlying Components of Sign.- Ensembles of Classifiers Derived from Multiple Prototypes and Their Application to Handwriting Recognition.- Network Intrusion Detection by a Multi-stage Classification System.- Application of Breiman’s Random Forest to Modeling Structure-Activity Relationships of Pharmaceutical Molecules.- Experimental Study on Multiple LDA Classifier Combination for High Dimensional Data Classification.- Physics-Based Decorrelation of Image Data for Decision Level Fusion in Face Verification.- High Security Fingerprint Verification by Perceptron-Based Fusion of Multiple Matchers.- Second Guessing a Commercial’Black Box’ Classifier by an’In House’ Classifier: Serial Classifier Combination in a Speech Recognition Application.