Multiple Classifier Systems: 4th International Workshop, MCS 2003 Guilford, UK, June 11-13, 2003 Proceedings (Lecture Notes in Computer Science) - Taschenbuch

EAN (ISBN-13): 9783540403692
ISBN (ISBN-10): 3540403698
Gebundene Ausgabe
Taschenbuch
Erscheinungsjahr: 2003
Herausgeber: Springer Berlin
424 Seiten
Gewicht: 0,637 kg
Sprache: eng/Englisch

Buch in der Datenbank seit 2007-09-24T06:33:40+02:00 (Berlin)
Detailseite zuletzt geändert am 2022-10-25T14:23:39+02:00 (Berlin)
ISBN/EAN: 9783540403692

ISBN - alternative Schreibweisen:
3-540-40369-8, 978-3-540-40369-2
Alternative Schreibweisen und verwandte Suchbegriffe:
Autor des Buches: fabio, windeatt
Titel des Buches: mcs

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Daten vom Verlag:

Autor/in: Terry Windeatt; Fabio Roli
Titel: Lecture Notes in Computer Science; Multiple Classifier Systems - 4th International Workshop, MCS 2003, Guilford, UK, June 11-13, 2003, Proceedings
Verlag: Springer; Springer Berlin
414 Seiten
Erscheinungsjahr: 2003-05-27
Berlin; Heidelberg; DE
Sprache: Englisch
53,49 € (DE)
54,99 € (AT)
59,00 CHF (CH)
Available
X, 414 p.

BC; Hardcover, Softcover / Informatik, EDV/Allgemeines, Lexika; Informationstechnologiebranche; Verstehen; Performance; algorithmic learning; algorithms; classification; classifier systems; cognition; document analysis; image analysis; learning; machine learning; multiple classifier systems; neural networks; proving; statistical learning; time series analysis; The Computer Industry; Automated Pattern Recognition; Theory of Computation; Artificial Intelligence; Computer Vision; Mustererkennung; Theoretische Informatik; Künstliche Intelligenz; Maschinelles Sehen, Bildverstehen; EA

Invited Paper.- Data Dependence in Combining Classifiers.- Boosting.- Boosting with Averaged Weight Vectors.- Error Bounds for Aggressive and Conservative AdaBoost.- An Empirical Comparison of Three Boosting Algorithms on Real Data Sets with Artificial Class Noise.- The Beneficial Effects of Using Multi-net Systems That Focus on Hard Patterns.- Combination Rules.- The Behavior Knowledge Space Fusion Method: Analysis of Generalization Error and Strategies for Performance Improvement.- Reducing the Overconfidence of Base Classifiers when Combining Their Decisions.- Linear Combiners for Classifier Fusion: Some Theoretical and Experimental Results.- Comparison of Classifier Selection Methods for Improving Committee Performance.- Towards Automated Classifier Combination for Pattern Recognition.- Multi-class Methods.- Serial Multiple Classifier Systems Exploiting a Coarse to Fine Output Coding.- Polychotomous Classification with Pairwise Classifiers: A New Voting Principle.- Multi-category Classification by Soft-Max Combination of Binary Classifiers.- A Sequential Scheduling Approach to Combining Multiple Object Classifiers Using Cross-Entropy.- Binary Classifier Fusion Based on the Basic Decomposition Methods.- Fusion Schemes Architectures.- Good Error Correcting Output Codes for Adaptive Multiclass Learning.- Finding Natural Clusters Using Multi-clusterer Combiner Based on Shared Nearest Neighbors.- An Ensemble Approach for Data Fusion with Learn++.- The Practical Performance Characteristics of Tomographically Filtered Multiple Classifier Fusion.- Accumulated-Recognition-Rate Normalization for Combining Multiple On/Off-Line Japanese Character Classifiers Tested on a Large Database.- Beam Search Extraction and Forgetting Strategies on Shared Ensembles.- A Markov Chain Approach to Multiple Classifier Fusion.- Neural Network Ensembles.- A Study of Ensemble of Hybrid Networks with Strong Regularization.- Combining Multiple Modes of Information Using Unsupervised Neural Classifiers.- Neural Net Ensembles for Lithology Recognition.- Improving Performance of a Multiple Classifier System Using Self-generating Neural Networks.- Ensemble Strategies.- Negative Correlation Learning and the Ambiguity Family of Ensemble Methods.- Spectral Coefficients and Classifier Correlation.- Ensemble Construction via Designed Output Distortion.- Simulating Classifier Outputs for Evaluating Parallel Combination Methods.- A New Ensemble Diversity Measure Applied to Thinning Ensembles.- Ensemble Methods for Noise Elimination in Classification Problems.- Applications.- New Boosting Algorithms for Classification Problems with Large Number of Classes Applied to a Handwritten Word Recognition Task.- Automatic Target Recognition Using Multiple Description Coding Models for Multiple Classifier Systems.- A Modular Multiple Classifier System for the Detection of Intrusions in Computer Networks.- Input Space Transformations for Multi-classifier Systems Based on n-tuple Classifiers with Application to Handwriting Recognition.- Building Classifier Ensembles for Automatic Sports Classification.- Classification of Aircraft Maneuvers for Fault Detection.- Solving Problems Two at a Time: Classification of Web Pages Using a Generic Pair-Wise Multiple Classifier System.- Design and Evaluation of an Adaptive Combination Framework for OCR Result Strings.