Multiple classifier systems : second International Workshop, MCS 2001, Cambridge, UK, July 2-4, 2001 : proceedings

Bagging and Boosting.- Bagging and the Random Subspace Method for Redundant Feature Spaces.- Performance Degradation in Boosting.- A Generalized Class of Boosting Algorithms Based on Recursive Decoding Models.- Tuning Cost-Sensitive Boosting and Its Application to Melanoma Diagnosis.- Learning Classification RBF Networks by Boosting.- MCS Design Methodology.- Data Complexity Analysis for Classifier Combination.- Genetic Programming for Improved Receiver Operating Characteristics.- Methods for Designing Multiple Classifier Systems.- Decision-Level Fusion in Fingerprint Verification.- Genetic Algorithms for Multi-classifier System Configuration: A Case Study in Character Recognition.- Combined Classification of Handwritten Digits Using the 'Virtual Test Sample Method'.- Averaging Weak Classifiers.- Mixing a Symbolic and a Subsymbolic Expert to Improve Carcinogenicity Prediction of Aromatic Compounds.- Ensemble Classifiers.- Multiple Classifier Systems Based on Interpretable Linear Classifiers.- Least Squares and Estimation Measures via Error Correcting Output Code.- Dependence among Codeword Bits Errors in ECOC Learning Machines: An Experimental Analysis.- Information Analysis of Multiple Classifier Fusion?.- Limiting the Number of Trees in Random Forests.- Learning-Data Selection Mechanism through Neural Networks Ensemble.- A Multi-SVM Classification System.- Automatic Classification of Clustered Microcalcifications by a Multiple Classifier System.- Feature Spaces for MCS.- Feature Weighted Ensemble Classifiers - A Modified Decision Scheme.- Feature Subsets for Classifier Combination: An Enumerative Experiment.- Input Decimation Ensembles: Decorrelation through Dimensionality Reduction.- Classifier Combination as a Tomographic Process.- MCS in Remote Sensing.- A Robust Multiple Classifier System for a Partially Unsupervised Updating of Land-Cover Maps.- Combining Supervised Remote Sensing Image Classifiers Based on Individual Class Performances.- Boosting, Bagging, and Consensus Based Classification of Multisource Remote Sensing Data.- Solar Wind Data Analysis Using Self-Organizing Hierarchical Neural Network Classifiers.- One Class MCS and Clustering.- Combining One-Class Classifiers.- Finding Consistent Clusters in Data Partitions.- A Self-Organising Approach to Multiple Classifier Fusion.- Combination Strategies.- Error Rejection in Linearly Combined Multiple Classifiers.- Relationship of Sum and Vote Fusion Strategies.- Complexity of Data Subsets Generated by the Random Subspace Method: An Experimental Investigation.- On Combining Dissimilarity Representations.- Application of Multiple Classifier Techniques to Subband Speaker Identification with an HMM/ANN System.- Classification of Time Series Utilizing Temporal and Decision Fusion.- Use of Positional Information in Sequence Alignment for Multiple Classifier Combination.- Application of the Evolutionary Algorithms for Classifier Selection in Multiple Classifier Systems with Majority Voting.- Tree-Structured Support Vector Machines for Multi-class Pattern Recognition.- On the Combination of Different Template Matching Strategies for Fast Face Detection.- Improving Product by Moderating k-NN Classifiers.- Automatic Model Selection in a Hybrid Perceptron/Radial Network.