Multiple Classifier Systems

Second International Workshop, MCS 2001 Cambridge, UK, July 2-4, 2001 Proceedings

AvJosef Kittler,Fabio Roli

Häftad, Engelska, 2001

Del i serien Lecture Notes in Computer Science

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Beskrivning

This book constitutes the refereed proceedings of the Second International Workshop on Multiple Classifier Systems, MCS 2001, held in Cambridge, UK in July 2001. The 44 revised papers presented were carefully reviewed and selected for presentation. The book offers topical sections on bagging and boosting, MCS design methodology, ensemble classifiers, feature spaces for MCS, MCS in remote sensing, one class MCS and clustering, and combination strategies.

Produktinformation

Utgivningsdatum:2001-06-20
Mått:155 x 235 x 26 mm
Vikt:715 g
Format:Häftad
Språk:Engelska
Serie:Lecture Notes in Computer Science
Antal sidor:456
Upplaga:2001
Förlag:Springer-Verlag Berlin and Heidelberg GmbH & Co. KG
ISBN:9783540422846

Utforska kategorier

Systemvetenskap och AI inom Data och IT

Innehållsförteckning

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.- ARobust 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.