+612 9045 4394
 
CHECKOUT
Multiple Classifier Systems : Second International Workshop, MCS 2001 Cambridge, Uk, July 2-4, 2001 Proceedings - Josef Kittler

Multiple Classifier Systems

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

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

Paperback Published: 20th June 2001
ISBN: 9783540422846
Number Of Pages: 456

Share This Book:

Paperback

$147.02
or 4 easy payments of $36.76 with Learn more
Ships in 5 to 9 business days

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.

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

ISBN: 9783540422846
ISBN-10: 3540422846
Series: Lecture Notes in Computer Science
Audience: General
Format: Paperback
Language: English
Number Of Pages: 456
Published: 20th June 2001
Publisher: SPRINGER VERLAG GMBH
Country of Publication: DE
Dimensions (cm): 23.39 x 15.6  x 2.44
Weight (kg): 0.66