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ORCID2. MCS 2001: Cambridge, UK
- Josef Kittler, Fabio Roli:
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
Bagging and Boosting
- Marina Skurichina, Robert P. W. Duin:
Bagging and the Random Subspace Method for Redundant Feature Spaces. 1-10 - Jeevani Wickramaratna, Sean B. Holden, Bernard F. Buxton:
Performance Degradation in Boosting. 11-21 - Elizabeth Tapia
, José Carlos González, Julio Villena:
A Generalized Class of Boosting Algorithms Based on Recursive Decoding Models. 22-31 - Stefano Merler, Cesare Furlanello, Barbara Larcher, Andrea Sboner:
Tuning Cost-Sensitive Boosting and Its Application to Melanoma Diagnosis. 32-42 - Juan J. Rodríguez Diez, Carlos Alonso González:
Learning Classification RBF Networks by Boosting. 43-52
MCS Design Methodology
- Tin Kam Ho:
Data Complexity Analysis for Classifier Combination. 53-67 - William B. Langdon, Bernard F. Buxton:
Genetic Programming for Improved Receiver Operating Characteristics. 68-77 - Fabio Roli, Giorgio Giacinto, Gianni Vernazza:
Methods for Designing Multiple Classifier Systems. 78-87 - Salil Prabhakar, Anil K. Jain:
Decision-Level Fusion in Fingerprint Verification. 88-98 - Konstantinos Sirlantzis, Michael C. Fairhurst, Sanaul Hoque:
Genetic Algorithms for Multi-classifier System Configuration: A Case Study in Character Recognition. 99-108 - Jörg Dahmen, Daniel Keysers, Hermann Ney:
Combined Classification of Handwritten Digits Using the 'Virtual Test Sample Method'. 109-118 - Dechang Chen, Jian Liu:
Averaging Weak Classifiers. 119-125 - 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
Ensemble Classifiers
- David J. Hand, Niall M. Adams, Mark G. Kelly:
Multiple Classifier Systems Based on Interpretable Linear Classifiers. 136-147 - Reza Ghaderi, Terry Windeatt:
Least Squares and Estimation Measures via Error Correcting Output Code. 148-157 - Francesco Masulli, Giorgio Valentini:
Dependence among Codeword Bits Errors in ECOC Learning Machines: An Experimental Analysis. 158-167 - Jirí Grim, Josef Kittler, Pavel Pudil, Petr Somol:
Information Analysis of Multiple Classifier Fusion. 168-177 - Patrice Latinne, Olivier Debeir, Christine Decaestecker:
Limiting the Number of Trees in Random Forests. 178-187 - Pitoyo Hartono, Shuji Hashimoto:
Learning-Data Selection Mechanism through Neural Networks Ensemble. 188-197 - Dimitrios S. Frossyniotis, Andreas Stafylopatis:
A Multi-SVM Classification System. 198-207 - Pasquale Foggia, Carlo Sansone, Francesco Tortorella, Mario Vento:
Automatic Classification of Clustered Microcalcifications by a Multiple Classifier System. 208-217
Feature Spaces for MCS
- Thomas Martini Jørgensen, Christian Linneberg:
Feature Weighted Ensemble Classifiers - A Modified Decision Scheme. 218-227 - Ludmila I. Kuncheva, Christopher J. Whitaker:
Feature Subsets for Classifier Combination: An Enumerative Experiment. 228-237 - Nikunj C. Oza, Kagan Tumer:
Input Decimation Ensembles: Decorrelation through Dimensionality Reduction. 238-247 - David Windridge, Josef Kittler:
Classifier Combination as a Tomographic Process. 248-258
MCS in Remote Sensing
- Lorenzo Bruzzone, Roberto Cossu:
A Robust Multiple Classifier System for a Partially Unsupervised Updating of Land-Cover Maps. 259-268 - Paul C. Smits:
Combining Supervised Remote Sensing Image Classifiers Based on Individual Class Performances. 269-278 - Gunnar Jakob Briem, Jón Atli Benediktsson, Johannes R. Sveinsson:
Boosting, Bagging, and Consensus Based Classification of Multisource Remote Sensing Data. 279-288 - 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
One Class MCS and Clustering
- David M. J. Tax, Robert P. W. Duin:
Combining One-Class Classifiers. 299-308 - Ana L. N. Fred:
Finding Consistent Clusters in Data Partitions. 309-318 - Stephen P. Luttrell:
A Self-Organising Approach to Multiple Classifier Fusion. 319-328
Combination Strategies
- Giorgio Fumera, Fabio Roli:
Error Rejection in Linearly Combined Multiple Classifiers. 329-338 - Josef Kittler, Fuad M. Alkoot:
Relationship of Sum and Vote Fusion Strategies. 339-348 - 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 - Elzbieta Pekalska, Robert P. W. Duin:
On Combining Dissimilarity Representations. 359-368 - 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 - Christian Dietrich, Friedhelm Schwenker, Günther Palm:
Classification of Time Series Utilizing Temporal and Decision Fusion. 378-387 - Urs-Viktor Marti, Horst Bunke:
Use of Positional Information in Sequence Alignment for Multiple Classifier Combination. 388-398 - Dymitr Ruta, Bogdan Gabrys:
Application of the Evolutionary Algorithms for Classifier Selection in Multiple Classifier Systems with Majority Voting. 399-408 - Friedhelm Schwenker, Günther Palm:
Tree-Structured Support Vector Machines for Multi-class Pattern Recognition. 409-417 - Bernhard Fröba, Walter Zink:
On the Combination of Different Template Matching Strategies for Fast Face Detection. 418-428 - Fuad M. Alkoot, Josef Kittler:
Improving Product by Moderating k-NN Classifiers. 429-439 - Shimon Cohen, Nathan Intrator:
Automatic Model Selection in a Hybrid Perceptron/Radial Network. 440-454
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