2. MCS 2001: Cambridge, UK

Josef Kittler, Fabio Roli (Eds.): Multiple Classifier Systems, Second International Workshop, MCS 2001 Cambridge, UK, July 2-4, 2001, Proceedings. Lecture Notes in Computer Science 2096 Springer 2001, ISBN 3-540-42284-6 BibTeX

@proceedings{DBLP:conf/mcs/2001,
  editor    = {Josef Kittler and
               Fabio Roli},
  title     = {Multiple Classifier Systems, Second International Workshop, MCS
               2001 Cambridge, UK, July 2-4, 2001, Proceedings},
  booktitle = {Multiple Classifier Systems},
  publisher = {Springer},
  series    = {Lecture Notes in Computer Science},
  volume    = {2096},
  year      = {2001},
  isbn      = {3-540-42284-6},
  bibsource = {DBLP, http://dblp.uni-trier.de}
}

Bagging and Boosting

Marina Skurichina, Robert P. W. Duin:
Bagging and the Random Subspace Method for Redundant Feature Spaces. 1-10
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Jeevani Wickramaratna, Sean B. Holden, Bernard F. Buxton:
Performance Degradation in Boosting. 11-21
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Elizabeth Tapia, José Carlos González, Julio Villena:
A Generalized Class of Boosting Algorithms Based on Recursive Decoding Models. 22-31
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Stefano Merler, Cesare Furlanello, Barbara Larcher, Andrea Sboner:
Tuning Cost-Sensitive Boosting and Its Application to Melanoma Diagnosis. 32-42
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Juan J. Rodríguez Diez, Carlos Alonso González:
Learning Classification RBF Networks by Boosting. 43-52
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MCS Design Methodology

Tin Kam Ho:
Data Complexity Analysis for Classifier Combination. 53-67
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William B. Langdon, Bernard F. Buxton:
Genetic Programming for Improved Receiver Operating Characteristics. 68-77
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Fabio Roli, Giorgio Giacinto, Gianni Vernazza:
Methods for Designing Multiple Classifier Systems. 78-87
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Salil Prabhakar, Anil K. Jain:
Decision-Level Fusion in Fingerprint Verification. 88-98
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Konstantinos Sirlantzis, Michael C. Fairhurst, Sanaul Hoque:
Genetic Algorithms for Multi-classifier System Configuration: A Case Study in Character Recognition. 99-108
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Jörg Dahmen, Daniel Keysers, Hermann Ney:
Combined Classification of Handwritten Digits Using the 'Virtual Test Sample Method'. 109-118
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Dechang Chen, Jian Liu:
Averaging Weak Classifiers. 119-125
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Giuseppina C. Gini, Marco Lorenzini, Emilio Benfenati, Raffaella Brambilla, Luca Malvé:
Mixing a Symbolic and a Subsymbolic Expert to Improve Carcinogenicity Prediction of Aromatic Compounds. 126-135
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Ensemble Classifiers

David J. Hand, Niall M. Adams, Mark G. Kelly:
Multiple Classifier Systems Based on Interpretable Linear Classifiers. 136-147
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Reza Ghaderi, Terry Windeatt:
Least Squares and Estimation Measures via Error Correcting Output Code. 148-157
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Francesco Masulli, Giorgio Valentini:
Dependence among Codeword Bits Errors in ECOC Learning Machines: An Experimental Analysis. 158-167
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Jiri Grim, Josef Kittler, Pavel Pudil, Petr Somol:
Information Analysis of Multiple Classifier Fusion. 168-177
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Patrice Latinne, Olivier Debeir, Christine Decaestecker:
Limiting the Number of Trees in Random Forests. 178-187
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Pitoyo Hartono, Shuji Hashimoto:
Learning-Data Selection Mechanism through Neural Networks Ensemble. 188-197
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Dimitrios S. Frossyniotis, Andreas Stafylopatis:
A Multi-SVM Classification System. 198-207
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Pasquale Foggia, Carlo Sansone, Francesco Tortorella, Mario Vento:
Automatic Classification of Clustered Microcalcifications by a Multiple Classifier System. 208-217
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Feature Spaces for MCS

Thomas Martini Jørgensen, Christian Linneberg:
Feature Weighted Ensemble Classifiers - A Modified Decision Scheme. 218-227
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Ludmila I. Kuncheva, Christopher J. Whitaker:
Feature Subsets for Classifier Combination: An Enumerative Experiment. 228-237
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Nikunj C. Oza, Kagan Tumer:
Input Decimation Ensembles: Decorrelation through Dimensionality Reduction. 238-247
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David Windridge, Josef Kittler:
Classifier Combination as a Tomographic Process. 248-258
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MCS in Remote Sensing

Lorenzo Bruzzone, Roberto Cossu:
A Robust Multiple Classifier System for a Partially Unsupervised Updating of Land-Cover Maps. 259-268
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Paul C. Smits:
Combining Supervised Remote Sensing Image Classifiers Based on Individual Class Performances. 269-278
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Gunnar Jakob Briem, Jon Atli Benediktsson, Johannes R. Sveinsson:
Boosting, Bagging, and Consensus Based Classification of Multisource Remote Sensing Data. 279-288
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S. A. Dolenko, Yu. V. Orlov, I. G. Persiantsev, Ju. S. Shugai, A. V. Dmitriev, A. V. Suvorova, I. S. Veselovsky:
Solar Wind Data Analysis Using Self-Organizing Hierarchical Neural Network Classifiers. 289-298
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One Class MCS and Clustering

David M. J. Tax, Robert P. W. Duin:
Combining One-Class Classifiers. 299-308
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Ana L. N. Fred:
Finding Consistent Clusters in Data Partitions. 309-318
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Stephen P. Luttrell:
A Self-Organising Approach to Multiple Classifier Fusion. 319-328
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Combination Strategies

Giorgio Fumera, Fabio Roli:
Error Rejection in Linearly Combined Multiple Classifiers. 329-338
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Josef Kittler, Fuad M. Alkoot:
Relationship of Sum and Vote Fusion Strategies. 339-348
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Ludmila I. Kuncheva, Fabio Roli, Gian Luca Marcialis, Catherine A. Shipp:
Complexity of Data Subsets Generated by the Random Subspace Method: An Experimental Investigation. 349-358
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Elzbieta Pekalska, Robert P. W. Duin:
On Combining Dissimilarity Representations. 359-368
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Jonathan E. Higgins, Tony J. Dodd, Robert I. Damper:
Application of Multiple Classifier Techniques to Subband Speaker Identification with an HMM/ANN System. 369-377
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Christian Dietrich, Friedhelm Schwenker, Günther Palm:
Classification of Time Series Utilizing Temporal and Decision Fusion. 378-387
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Urs-Viktor Marti, Horst Bunke:
Use of Positional Information in Sequence Alignment for Multiple Classifier Combination. 388-398
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Dymitr Ruta, Bogdan Gabrys:
Application of the Evolutionary Algorithms for Classifier Selection in Multiple Classifier Systems with Majority Voting. 399-408
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Friedhelm Schwenker, Günther Palm:
Tree-Structured Support Vector Machines for Multi-class Pattern Recognition. 409-417
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Bernhard Fröba, Walter Zink:
On the Combination of Different Template Matching Strategies for Fast Face Detection. 418-428
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Fuad M. Alkoot, Josef Kittler:
Improving Product by Moderating k-NN Classifiers. 429-439
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Shimon Cohen, Nathan Intrator:
Automatic Model Selection in a Hybrid Perceptron/Radial Network. 440-454
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