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ORCID4. MCS 2003: Guilford, UK
- Terry Windeatt
, Fabio Roli:
Multiple Classifier Systems, 4th International Workshop, MCS 2003, Guilford, UK, June 11-13, 2003, Proceedings. Lecture Notes in Computer Science 2709, Springer 2003, ISBN 3-540-40369-8
Invited Paper
- Mohamed S. Kamel, Nayer M. Wanas:
Data Dependence in Combining Classifiers. 1-14
Boosting
- Nikunj C. Oza:
Boosting with Averaged Weight Vectors. 15-24 - Ludmila I. Kuncheva:
Error Bounds for Aggressive and Conservative AdaBoost. 25-34 - Ross A. McDonald, David J. Hand, Idris A. Eckley:
An Empirical Comparison of Three Boosting Algorithms on Real Data Sets with Artificial Class Noise. 35-44 - Jerónimo Arenas-García, Aníbal R. Figueiras-Vidal, Amanda J. C. Sharkey:
The Beneficial Effects of Using Multi-net Systems That Focus on Hard Patterns. 45-54
Combination Rules
- Sarunas Raudys, Fabio Roli:
The Behavior Knowledge Space Fusion Method: Analysis of Generalization Error and Strategies for Performance Improvement. 55-64 - Sarunas Raudys, Ray L. Somorjai, Richard Baumgartner:
Reducing the Overconfidence of Base Classifiers when Combining Their Decisions. 65-73 - Giorgio Fumera, Fabio Roli:
Linear Combiners for Classifier Fusion: Some Theoretical and Experimental Results. 74-83 - Matti Aksela:
Comparison of Classifier Selection Methods for Improving Committee Performance. 84-93 - Alper Baykut, Aytül Erçil:
Towards Automated Classifier Combination for Pattern Recognition. 94-105
Multi-class Methods
- Josef Kittler, Alireza Ahmadyfard, David Windridge:
Serial Multiple Classifier Systems Exploiting a Coarse to Fine Output Coding. 106-114 - Florin Cutzu:
Polychotomous Classification with Pairwise Classifiers: A New Voting Principle. 115-124 - Kaibo Duan, S. Sathiya Keerthi, Wei Chu, Shirish Krishnaj Shevade, Aun Neow Poo:
Multi-category Classification by Soft-Max Combination of Binary Classifiers. 125-134 - Derek R. Magee:
A Sequential Scheduling Approach to Combining Multiple Object Classifiers Using Cross-Entropy. 135-145 - Jaepil Ko, Hyeran Byun:
Binary Classifier Fusion Based on the Basic Decomposition Methods. 146-155 - Elizabeth Tapia, José Carlos González, L. Javier García-Villalba:
Good Error Correcting Output Codes for Adaptive Multiclass Learning. 156-165
Fusion Schemes Architectures
- Hanan Ayad, Mohamed S. Kamel:
Finding Natural Clusters Using Multi-clusterer Combiner Based on Shared Nearest Neighbors. 166-175 - Michael Lewitt, Robi Polikar:
An Ensemble Approach for Data Fusion with Learn++. 176-185 - David Windridge, Josef Kittler:
The Practical Performance Characteristics of Tomographically Filtered Multiple Classifier Fusion. 186-195 - Ondrej Velek, Stefan Jäger, Masaki Nakagawa:
Accumulated-Recognition-Rate Normalization for Combining Multiple On/Off-Line Japanese Character Classifiers Tested on a Large Database. 196-205 - Vicent Estruch, César Ferri, José Hernández-Orallo, M. José Ramírez-Quintana:
Beam Search Extraction and Forgetting Strategies on Shared Ensembles. 206-216 - Stephen P. Luttrell:
A Markov Chain Approach to Multiple Classifier Fusion. 217-226
Neural Network Ensembles
- Shimon Cohen, Nathan Intrator:
A Study of Ensemble of Hybrid Networks with Strong Regularization. 227-235 - Khurshid Ahmad, Matthew C. Casey, Bogdan Vrusias, Panagiotis Saragiotis:
Combining Multiple Modes of Information Using Unsupervised Neural Classifiers. 236-245 - Rafael Valle dos Santos, Marley M. B. R. Vellasco, Fredy Artola, Sérgio da Fontoura:
Neural Net Ensembles for Lithology Recognition. 246-255 - Hirotaka Inoue, Hiroyuki Narihisa:
Improving Performance of a Multiple Classifier System Using Self-generating Neural Networks. 256-265
Ensemble Strategies
- Gavin Brown, Jeremy L. Wyatt:
Negative Correlation Learning and the Ambiguity Family of Ensemble Methods. 266-275 - Terry Windeatt, Reza Ghaderi, Gholamreza Ardeshir:
Spectral Coefficients and Classifier Correlation. 276-285 - Stefan W. Christensen:
Ensemble Construction via Designed Output Distortion. 286-295 - Héla Zouari, Laurent Heutte, Yves Lecourtier, Adel M. Alimi:
Simulating Classifier Outputs for Evaluating Parallel Combination Methods. 296-305 - Robert E. Banfield, Lawrence O. Hall, Kevin W. Bowyer, W. Philip Kegelmeyer:
A New Ensemble Diversity Measure Applied to Thinning Ensembles. 306-316 - Sofie Verbaeten, Anneleen Van Assche:
Ensemble Methods for Noise Elimination in Classification Problems. 317-325
Applications
- Simon Günter, Horst Bunke:
New Boosting Algorithms for Classification Problems with Large Number of Classes Applied to a Handwritten Word Recognition Task. 326-335 - Widhyakorn Asdornwised, Somchai Jitapunkul:
Automatic Target Recognition Using Multiple Description Coding Models for Multiple Classifier Systems. 336-345 - Giorgio Giacinto, Fabio Roli, Luca Didaci:
A Modular Multiple Classifier System for the Detection of Intrusions in Computer Networks. 346-355 - Konstantinos Sirlantzis, Sanaul Hoque, Michael C. Fairhurst:
Input Space Transformations for Multi-classifier Systems Based on n-tuple Classifiers with Application to Handwriting Recognition. 356-365 - Edward Jaser, Josef Kittler, William J. Christmas:
Building Classifier Ensembles for Automatic Sports Classification. 366-374 - Nikunj C. Oza, Kagan Tumer, Irem Y. Tumer, Edward M. Huff:
Classification of Aircraft Maneuvers for Fault Detection. 375-384 - Hassan Alam, Ahmad Fuad Rezaur Rahman, Yuliya Tarnikova:
Solving Problems Two at a Time: Classification of Web Pages Using a Generic Pair-Wise Multiple Classifier System. 385-394 - Elke Wilczok, Wolfgang Lellmann:
Design and Evaluation of an Adaptive Combination Framework for OCR Result Strings. 395-404
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