Multiple Classifier Systems [electronic resource] : 5th International Workshop, MCS 2004, Cagliari, Italy, June 9-11, 2004, Proceedings / edited by Fabio Roli, Josef Kittler, Terry Windeatt.

1st ed. 2004.
Berlin, Heidelberg : Springer Berlin Heidelberg : Imprint: Springer, 2004.
Computer Science (Springer-11645)
Lecture notes in computer science 0302-9743 ; 3077
Lecture Notes in Computer Science, 0302-9743 ; 3077
1 online resource (XII, 392 pages)
Artificial intelligence.
Pattern perception.
Optical data processing.
Local subjects:
Artificial Intelligence.
Pattern Recognition.
Image Processing and Computer Vision.
Computation by Abstract Devices.
System Details:
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The fusion of di?erent information sourcesis a persistent and intriguing issue. It hasbeenaddressedforcenturiesinvariousdisciplines,includingpoliticalscience, probability and statistics, system reliability assessment, computer science, and distributed detection in communications. Early seminal work on fusion was c- ried out by pioneers such as Laplace and von Neumann. More recently, research activities in information fusion have focused on pattern recognition. During the 1990s,classi?erfusionschemes,especiallyattheso-calleddecision-level,emerged under a plethora of di?erent names in various scienti?c communities, including machine learning, neural networks, pattern recognition, and statistics. The d- ferent nomenclatures introduced by these communities re?ected their di?erent perspectives and cultural backgrounds as well as the absence of common forums and the poor dissemination of the most important results. In 1999, the ?rst workshop on multiple classi?er systems was organized with the main goal of creating a common international forum to promote the diss- ination of the results achieved in the diverse communities and the adoption of a common terminology, thus giving the di?erent perspectives and cultural ba- grounds some concrete added value. After ?ve meetings of this workshop, there is strong evidence that signi?cant steps have been made towards this goal. - searchers from these diverse communities successfully participated in the wo- shops, and world experts presented surveys of the state of the art from the perspectives of their communities to aid cross-fertilization.
Invited Papers
Classifier Ensembles for Changing Environments
A Generic Sensor Fusion Problem: Classification and Function Estimation
Bagging and Boosting
AveBoost2: Boosting for Noisy Data
Bagging Decision Multi-trees
Learn++.MT: A New Approach to Incremental Learning
Beyond Boosting: Recursive ECOC Learning Machines
Exact Bagging with k-Nearest Neighbour Classifiers
Combination Methods
Yet Another Method for Combining Classifiers Outputs: A Maximum Entropy Approach
Combining One-Class Classifiers to Classify Missing Data
Combining Kernel Information for Support Vector Classification
Combining Classifiers Using Dependency-Based Product Approximation with Bayes Error Rate
Combining Dissimilarity-Based One-Class Classifiers
A Modular System for the Classification of Time Series Data
A Probabilistic Model Using Information Theoretic Measures for Cluster Ensembles
Classifier Fusion Using Triangular Norms
Dynamic Integration of Regression Models
Dynamic Classifier Selection by Adaptive k-Nearest-Neighbourhood Rule
Design Methods
Spectral Measure for Multi-class Problems
The Relationship between Classifier Factorisation and Performance in Stochastic Vector Quantisation
A Method for Designing Cost-Sensitive ECOC
Building Graph-Based Classifier Ensembles by Random Node Selection
A Comparison of Ensemble Creation Techniques
Multiple Classifiers System for Reducing Influences of Atypical Observations
Sharing Training Patterns among Multiple Classifiers
Performance Analysis
First Experiments on Ensembles of Radial Basis Functions
Random Aggregated and Bagged Ensembles of SVMs: An Empirical Bias-Variance Analysis
Building Diverse Classifier Outputs to Evaluate the Behavior of Combination Methods: The Case of Two Classifiers
An Empirical Comparison of Hierarchical vs. Two-Level Approaches to Multiclass Problems
Experiments on Ensembles with Missing and Noisy Data
Induced Decision Fusion in Automated Sign Language Interpretation: Using ICA to Isolate the Underlying Components of Sign
Ensembles of Classifiers Derived from Multiple Prototypes and Their Application to Handwriting Recognition
Network Intrusion Detection by a Multi-stage Classification System
Application of Breiman's Random Forest to Modeling Structure-Activity Relationships of Pharmaceutical Molecules
Experimental Study on Multiple LDA Classifier Combination for High Dimensional Data Classification
Physics-Based Decorrelation of Image Data for Decision Level Fusion in Face Verification
High Security Fingerprint Verification by Perceptron-Based Fusion of Multiple Matchers
Second Guessing a Commercial'Black Box' Classifier by an'In House' Classifier: Serial Classifier Combination in a Speech Recognition Application.
Roli, Fabio. editor., Editor,
Kittler, Josef. editor., Editor,
Windeatt, Terry, editor., Editor,
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