1. MCS 2000: Cagliari, Italy

Invited Papers

• Thomas G. Dietterich:
  Ensemble Methods in Machine Learning. 1-15
  
• Robert P. W. Duin, David M. J. Tax:
  Experiments with Classifier Combining Rules. 16-29
  
• Amanda J. C. Sharkey, Noel E. Sharkey, Uwe Gerecke, Gopinath Odayammadath Chandroth:
  The "Test and Select" Approach to Ensemble Combination. 30-44
  
• Sargur N. Srihari:
  A Survey of Sequential Combination of Word Recognizers in Handwritten Phrase Recognition at CEDAR. 45-51
  
• Ching Y. Suen, Louisa Lam:
  Multiple Classifier Combination Methodologies for Different Output Levels. 52-66
  

Theoretical Issues

• Eugene M. Kleinberg:
  A Mathematically Rigorous Foundation for Supervised Learning. 67-76
  
• Louisa Lam:
  Classifier Combinations: Implementations and Theoretical Issues. 77-86
  
• Wenxin Jiang:
  Some Results on Weakly Accurate Base Learners for Boosting Regression and Classification. 87-96
  

Multiple Classifier Fusion

• Tin Kam Ho:
  Complexity of Classification Problems and Comparative Advantages of Combined Classifiers. 97-106
  
• Francesco Masulli, Giorgio Valentini:
  Effectiveness of Error Correcting Output Codes in Multiclass Learning Problems. 107-116
  
• Elzbieta Pekalska, Marina Skurichina, Robert P. W. Duin:
  Combining Fisher Linear Discriminants for Dissimilarity Representations. 117-126
  
• Katsuhiko Takahashi, Atsushi Sato:
  A Learning Method of Feature Selection for Rough Classification. 127-136
  
• Mark D. Happel, Peter Bock:
  Analysis of a Fusion Method for Combining Marginal Classifiers. 137-146
  
• Shimon Cohen, Nathan Intrator:
  A Hybrid Projection Based and Radial Basis Function Architecture. 147-156
  
• Jiri Grim, Josef Kittler, Pavel Pudil, Petr Somol:
  Combining Multiple Classifiers in Probabilistic Neural Networks. 157-166
  
• Claudio Conversano, Roberta Siciliano, Francesco Mola:
  Supervised Classifier Combination through Generalized Additive Multi-model. 167-176
  
• Giorgio Giacinto, Fabio Roli:
  Dynamic Classifier Selection. 177-189
  

Bagging and Boosting

• Marina Skurichina, Robert P. W. Duin:
  Boosting in Linear Discriminant Analysis. 190-199
  
• Patrice Latinne, Olivier Debeir, Christine Decaestecker:
  Different Ways of Weakening Decision Trees and Their Impact on Classification Accuracy of DT Combination. 200-209
  
• Juan J. Rodríguez Diez, Carlos Alonso González:
  Applying Boosting to Similarity Literals for Time Series Classification. 210-219
  
• Cesare Furlanello, Stefano Merler:
  Boosting of Tree-Based Classifiers for Predictive Risk Modeling in GIS. 220-229
  

Design of Multiple Classifier Systems

• Sebastiano Impedovo, A. Salzo:
  A New Evaluation Method for Expert Combination in Multi-expert System Designing. 230-239
  
• Wenjia Wang, Phillis Jones, Derek Partridge:
  Diversity between Neural Networks and Decision Trees for Building Multiple Classifier Systems. 240-249
  
• Niall Griffith, Derek Partridge:
  Self-Organizing Decomposition of Functions in the Context of a Unified Framework for Multiple Classifier Systems. 250-259
  
• Terry Windeatt:
  Classifier Instability and Partitioning. 260-269
  

Applications of Multiple Classifier Systems: Remote-Sensing Data Analysis

• Shailesh Kumar, Joydeep Ghosh, Melba M. Crawford:
  A Hierarchical Multiclassifier System for Hyperspectral Data Analysis. 270-279
  
• Jon Atli Benediktsson, Johannes R. Sveinsson:
  Consensus Based Classification of Multisource Remote Sensing Data. 280-289
  
• Lorenzo Bruzzone, Roberto Cossu, Diego Fernández-Prieto:
  Combining Parametric and Nonparametric Classifiers for an Unsupervised Updating of Land-Cover Maps. 290-299
  
• Weijian Wan, Donald Fraser:
  A Multiple Self-Organizing Map Scheme for Remote Sensing Classification. 300-309
  

Applications of Multiple Classifier Systems: Document Analysis

• Petr Slavík, Venu Govindaraju:
  Use of Lexicon Density in Evaluating Word Recognizers. 310-319
  
• Vincenzo Di Lecce, Giovanni Dimauro, Andrea Guerriero, Sebastiano Impedovo, Giuseppe Pirlo, A. Salzo:
  A Multi-expert System for Dynamic Signature Verification. 320-329
  
• Luigi P. Cordella, Pasquale Foggia, Carlo Sansone, Francesco Tortorella, Mario Vento:
  A Cascaded Multiple Expert System for Verification. 330-339
  
• Krassimir G. Ianakiev, Venu Govindaraju:
  Architecture for Classifier Combination Using Entropy Measures. 340-350
  

Applications of Multiple Classifier Systems: Miscellaneous Applications

• Raffaele Cappelli, Dario Maio, Davide Maltoni:
  Combining Fingerprint Classifiers. 351-361
  
• Bernhard Fröba, Constanze Rothe, Christian Küblbeck:
  Statistical Sensor Calibration for Fusion of Different Classifiers in a Biometric Person Recognition Framework. 362-371
  
• Anna Maria Fanelli, Giovanna Castellano, Cosimo Alessandro Buscicchio:
  A Modular Neuro-Fuzzy Network for Musical Instruments Classification. 372-382
  
• Xiaoyi Jiang, Keren Yu, Horst Bunke:
  Classifier Combination for Grammar-Guided Sentence Recognition. 383-392
  
• Itsuo Kumazawa:
  Shape Matching and Extraction by an Array of Figure-and-Ground Classifiers. 393-402
  

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