9. MCS 2010: Cairo, Egypt

Classifier Ensembles(I)

• Santi Seguí, Laura Igual, Jordi Vitrià:
  Weighted Bagging for Graph Based One-Class Classifiers. 1-10
  
• Muhammad Atif Tahir, Josef Kittler, Krystian Mikolajczyk, Fei Yan:
  Improving Multilabel Classification Performance by Using Ensemble of Multi-label Classifiers. 11-21
  
• Ahmed Salah El-Din, Neamat El Gayar:
  New Feature Splitting Criteria for Co-training Using Genetic Algorithm Optimization. 22-32
  
• Gregory Ditzler, Michael D. Muhlbaier, Robi Polikar:
  Incremental Learning of New Classes in Unbalanced Datasets: Learn ^{+ +} .UDNC. 33-42
  
• David Windridge:
  Tomographic Considerations in Ensemble Bias/Variance Decomposition. 43-53
  
• Ludmila I. Kuncheva, Catrin O. Plumpton:
  Choosing Parameters for Random Subspace Ensembles for fMRI Classification. 54-63
  

Classifier Ensembles(II)

• Juan José Rodríguez, Cesar García-Osorio, Jesús Maudes, José-Francisco Díez-Pastor:
  An Experimental Study on Ensembles of Functional Trees. 64-73
  
• Battista Biggio, Giorgio Fumera, Fabio Roli:
  Multiple Classifier Systems under Attack. 74-83
  
• Francesco Gargiulo, Carlo Sansone:
  SOCIAL: Self-Organizing ClassIfier ensemble for Adversarial Learning. 84-93
  
• Giulia Troglio, Marina Alberti, Jon Atli Benediktsson, Gabriele Moser, Sebastiano B. Serpico, Einar Stefánsson:
  Unsupervised Change-Detection in Retinal Images by a Multiple-Classifier Approach. 94-103
  
• Víctor Soto, Gonzalo Martínez-Muñoz, Daniel Hernández-Lobato, Alberto Suárez:
  A Double Pruning Algorithm for Classification Ensembles. 104-113
  
• Krzysztof Kryszczuk, Paul Hurley:
  Estimation of the Number of Clusters Using Multiple Clustering Validity Indices. 114-123
  

Classifier Diversity

• Gavin Brown, Ludmila I. Kuncheva:
  "Good" and "Bad" Diversity in Majority Vote Ensembles. 124-133
  
• Zhi-Hua Zhou, Nan Li:
  Multi-information Ensemble Diversity. 134-144
  

Classifier Selection

• Paulo Rodrigo Cavalin, Robert Sabourin, Ching Y. Suen:
  Dynamic Selection of Ensembles of Classifiers Using Contextual Information. 145-154
  
• Wan-Jui Lee, Robert P. W. Duin, Horst Bunke:
  Selecting Structural Base Classifiers for Graph-Based Multiple Classifier Systems. 155-164
  

Combining Multiple Kernels

• Alexander Tatarchuk, Eugene Urlov, Vadim Mottl, David Windridge:
  A Support Kernel Machine for Supervised Selective Combining of Diverse Pattern-Recognition Modalities. 165-174
  
• Fei Yan, Krystian Mikolajczyk, Josef Kittler, Muhammad Atif Tahir:
  Combining Multiple Kernels by Augmenting the Kernel Matrix. 175-184
  

Boosting and Bootstrapping

• Raymond S. Smith, Terry Windeatt:
  Class-Separability Weighting and Bootstrapping in Error Correcting Output Code Ensembles. 185-194
  
• Oriol Pujol:
  Boosted Geometry-Based Ensembles. 195-204
  
• Adam Pocock, Paraskevas Yiapanis, Jeremy Singer, Mikel Luján, Gavin Brown:
  Online Non-stationary Boosting. 205-214
  

Handwriting Recognition

• Volkmar Frinken, Andreas Fischer, Horst Bunke:
  Combining Neural Networks to Improve Performance of Handwritten Keyword Spotting. 215-224
  
• Mohamed Farouk Abdel Hady, Friedhelm Schwenker:
  Combining Committee-Based Semi-supervised and Active Learning and Its Application to Handwritten Digits Recognition. 225-234
  
• Nabiha Azizi, Nadir Farah, Mokhtar Sellami, Abdel Ennaji:
  Using Diversity in Classifier Set Selection for Arabic Handwritten Recognition. 235-244
  

Applications

• Waleed M. Azmy, Amir F. Atiya, Hisham El-Shishiny:
  Forecast Combination Strategies for Handling Structural Breaks for Time Series Forecasting. 245-253
  
• Farhad Samadzadegan, Behnaz Bigdeli, Pouria Ramzi:
  A Multiple Classifier System for Classification of LIDAR Remote Sensing Data Using Multi-class SVM. 254-263
  
• Luana Batista, Eric Granger, Robert Sabourin:
  A Multi-Classifier System for Off-Line Signature Verification Based on Dissimilarity Representation. 264-273
  
• Noha A. Yousri:
  A Multi-objective Sequential Ensemble for Cluster Structure Analysis and Visualization and Application to Gene Expression. 274-283
  
• Carlo Sansone, Vincenzo Paduano, Michele Ceccarelli:
  Combining 2D and 3D Features to Classify Protein Mutants in HeLa Cells. 284-293
  
• Matteo Re, Giorgio Valentini:
  An Experimental Comparison of Hierarchical Bayes and True Path Rule Ensembles for Protein Function Prediction. 294-303
  
• Mahmoud Khademi, Mohammad Taghi Manzuri Shalmani, Mohammad Hadi Kiapour, Ali Akbar Kiaei:
  Recognizing Combinations of Facial Action Units with Different Intensity Using a Mixture of Hidden Markov Models and Neural Network. 304-313
  

Invited Papers

• Gavin Brown:
  Some Thoughts at the Interface of Ensemble Methods and Feature Selection. 314
  
• Friedhelm Schwenker, Stefan Scherer, Miriam Schmidt, Martin Schels, Michael Glodek:
  Multiple Classifier Systems for the Recogonition of Human Emotions. 315-324
  

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