Multiple Classifier Systems: 7th International Workshop, Mcs 2007, Prague, Czech Republic, May 23-25, 2007, Proceedings

by Haindl, Michal; Kittler, Josef; Roli, Fabio

• ISBN13: 9783540724810
• ISBN10: 3540724818
• Format: Paperback
• Copyright: 2007-06-12
• Publisher: Springer Verlag

Summary

This book constitutes the refereed proceedings of the 7th International Workshop on Multiple Classifier Systems, MCS 2007, held in Prague, Czech Republic in May 2007. The 49 revised full papers presented together with 2 invited papers were carefully reviewed and selected from more than 80 initial submissions. The papers are organized in topical sections on kernel-based fusion, applications, boosting, cluster and graph ensembles, feature subspace ensembles, multiple classifier system theory, intramodal and multimodal fusion of biometric experts, majority voting, and ensemble learning.

Table of Contents

Combining pattern recognition modalities at the sensor level via kernel fusion	p. 1
The neutral point method for kernel-based combination of disjoint training data in multi-modal pattern recognition	p. 13
Kernel combination versus classifier combination	p. 22
Deriving the kernel from training data	p. 32
On the application of SVM-ensembles based on adapted random subspace sampling for automatic classification of NMR data	p. 42
A new HMM-based ensemble generation method for numeral recognition	p. 52
Classifiers fusion in recognition of wheat varieties	p. 62
Multiple classifier methods for offline handwritten text line recognition	p. 72
Applying data fusion methods to passage retrieval in QAS	p. 82
A co-training approach for time series prediction with missing data	p. 93
An improved random subspace method and its application to EEG signal classification	p. 103
Ensemble learning methods for classifying EEG signals	p. 113
Confidence based gating of colour features for face authentication	p. 121
View-based Eigenspaces with mixture of experts for view-independent face recognition	p. 131
Fusion of support vector classifiers for parallel Gabor methods applied to face verification	p. 141
Serial fusion of fingerprint and face matchers	p. 151
Boosting lite - handling larger datasets and slower base classifiers	p. 161
Information theoretic combination of classifiers with application to AdaBoost	p. 171
Interactive boosting for image classification	p. 180
Group-induced vector spaces	p. 190
Selecting diversifying heuristics for cluster ensembles	p. 200
Unsupervised texture segmentation using multiple segmenters strategy	p. 210
Classifier ensembles for vector space embedding of graphs	p. 220
Cascading for nominal data	p. 231
A combination of sample subsets and feature subsets in one-against-other classifiers	p. 241
Random feature subset selection for ensemble based classification of data with missing features	p. 251
Feature subspace ensembles : a parallel classifier combination scheme using feature selection	p. 261
Stopping criteria for ensemble-based feature selection	p. 271
On rejecting unreliably classified patterns	p. 282
Bayesian analysis of linear combiners	p. 292
Applying pairwise fusion matrix on fusion functions for classifier combination	p. 302
Modelling multiple-classifier relationships using Bayesian belief networks	p. 312
Classifier combining rules under independence assumptions	p. 322
Embedding reject option in ECOC through LDPC codes	p. 333
On combination of face authentication experts by a mixture of quality dependent fusion classifiers	p. 344
Index driven combination of multiple biometric experts for AUC maximisation	p. 357
Q - stack : uni- and multimodal classifier stacking with quality measures	p. 367
Reliability-based voting schemes using modality-independent features in multi-classifier biometric authentication	p. 377
Optimal classifier combination rules for verification and identification systems	p. 387
Exploiting diversity in ensembles : improving the performance on unbalanced datasets	p. 397
On the diversity-performance relationship for majority voting in classifier ensembles	p. 407
Hierarchical behavior knowledge space	p. 421
A new dynamic ensemble selection method for numeral recognition	p. 431
Ensemble learning in linearly combined classifiers via negative correlation	p. 440
Naive Bayes ensembles with a random oracle	p. 450
An experimental study on rotation forest ensembles	p. 459
Cooperative coevolutionary ensemble learning	p. 469
Robust inference in Bayesian networks with application to gene expression temporal data	p. 479
An ensemble approach for incremental learning in nonstationary environments	p. 490
Multiple classifier systems in remote sensing : from basics to recent developments	p. 501
Biometric person authentication is a multiple classifier problem	p. 513
Table of Contents provided by Blackwell. All Rights Reserved.