Multiple Classifier Systems

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

By: Josef Kittler (Editor), Fabio Roli (Editor)

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Paperback | 20 June 2001

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Driven by the requirements of a large number of practical and commercially - portant applications, the last decade has witnessed considerable advances in p- tern recognition. Better understanding of the design issues and new paradigms, such as the Support Vector Machine, have contributed to the development of - proved methods of pattern classi cation. However, while any performance gains are welcome, and often extremely signi cant from the practical point of view, it is increasingly more challenging to reach the point of perfection as de ned by the theoretical optimality of decision making in a given decision framework. The asymptoticity of gains that can be made for a single classi er is a re?- tion of the fact that any particular design, regardless of how good it is, simply provides just one estimate of the optimal decision rule. This observation has motivated the recent interest in Multiple Classi er Systems , which aim to make use of several designs jointly to obtain a better estimate of the optimal decision boundary and thus improve the system performance. This volume contains the proceedings of the international workshop on Multiple Classi er Systems held at Robinson College, Cambridge, United Kingdom (July 2{4, 2001), which was organized to provide a forum for researchers in this subject area to exchange views and report their latest results.

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You Can Find This Book In

Non-Fiction Computing & I.T.Computer Science Mathematical Theory of Computation Psychology Cognition & Cognitive Psychology Artificial Intelligence Neural Networks & Fuzzy Systems Medicine Medicine in General Computer Vision Machine Learning

Pattern Recognition Image Processing Computer Programming & Software Development Algorithms & Data Structures Graphical & Digital Media Applications Databases Data Capture & Analysis

Bagging and the Random Subspace Method for Redundant Feature Spaces	p. 1
Performance Degradation in Boosting	p. 11
A Generalized Class of Boosting Algorithms Based on Recursive Decoding Models	p. 22
Tuning Cost-Sensitive Boosting and Its Application to Melanoma Diagnosis	p. 32
Learning Classification RBF Networks by Boosting	p. 43
Data Complexity Analysis for Classifier Combination	p. 53
Genetic Programming for Improved Receiver Operating Characteristics	p. 68
Methods for Designing Multiple Classifier Systems	p. 78
Decision-Level Fusion in Fingerprint Verification	p. 88
Genetic Algorithms for Multi-classifier System Configuration: A Case Study in Character Recognition	p. 99
Combined Classification of Handwritten Digits Using the 'Virtual Test Sample Method'	p. 109
Averaging Weak Classifiers	p. 119
Mixing a Symbolic and a Subsymbolic Expert to Improve Carcinogenicity Prediction of Aromatic Compounds	p. 126
Multiple Classifier Systems Based on Interpretable Linear Classifiers	p. 136
Least Squares and Estimation Measures via Error Correcting Output Code	p. 148
Dependence among Codeword Bits Errors in ECOC Learning Machines: An Experimental Analysis	p. 158
Information Analysis of Multiple Classifier Fusion	p. 168
Limiting the Number of Trees in Random Forests	p. 178
Learning-Data Selection Mechanism through Neural Networks Ensemble	p. 188
A Multi-SVM Classification System	p. 198
Automatic Classification of Clustered Microcalcifications by a Multiple Classifier System	p. 208
Feature Weighted Ensemble Classifiers - A Modified Decision Scheme	p. 218
Feature Subsets for Classifier Combination: An Enumerative Experiment	p. 228
Input Decimation Ensembles: Decorrelating through Dimensionality Reduction	p. 238
Classifier Combination as a Tomographic Process	p. 248
A Robust Multiple Classifier System for a Partially Unsuprevised Updating of Land-Cover Maps	p. 259
Combining Supervised Remote Sensing Image Classifiers Based on Individual Class Performances	p. 269
Boosting, Bagging, and Consensus Based Classification of Multisource Remote Sensing Data	p. 279
Solar Wind Data Analysis Using Self-Organizing Hierarchical Neural Network Classifiers	p. 289
Combining One-Class Classifiers	p. 299
Finding Consistent Clusters in Data Partitions	p. 309
A Self-Organising Approach to Multiple Classifier Fusion	p. 319
Error Rejection in Linearly Combined Multiple Classifiers	p. 329
Relationship of Sum and Vote Fusion Strategies	p. 339
Complexity of Data Subsets Generated by the Random Subspace Method: An Experimental Investigation	p. 349
On Combining Dissimilarity Representations	p. 359
Application of Multiple Classifier Techniques to Subband Speaker Identification with an HMM/ANN System	p. 369
Classification of Time Series Utilizing Temporal and Decision Fusion	p. 378
Use of Positional Information in Sequence Alignment for Multiple Classifier Combination	p. 388
Application of the Evolutionary Algorithms for Classifier Selection in Multiple Classifier Systems with Majority Voting	p. 399
Tree Structure Support Vector Machines for Multi-class Pattern Recognition	p. 409
On the Combination of Different Template Matching Strategies for Fast Face Detection	p. 418
Improving Product by Moderating k-NN Classifiers	p. 429
Automatic Model Selection in a Hybrid Perceptron/Radial Network	p. 440
Author Index	p. 455
Table of Contents provided by Blackwell. All Rights Reserved.

Multiple Classifier Systems

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

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