Introduction to statistical pattern recognition (2nd ed.)
Introduction to statistical pattern recognition (2nd ed.)
Neural networks and the bias/variance dilemma
Neural Computation
Original Contribution: Stacked generalization
Neural Networks
Statistical and neural classifiers: an integrated approach to design
Statistical and neural classifiers: an integrated approach to design
Neural Networks: A Comprehensive Foundation
Neural Networks: A Comprehensive Foundation
Pattern Recognition and Neural Networks
Pattern Recognition and Neural Networks
Proceedings of the Second International Workshop on Multiple Classifier Systems
MCS '01 Proceedings of the Second International Workshop on Multiple Classifier Systems
Comparison of Two Classification Methodologies on a Real-World Biomedical Problem
Proceedings of the Joint IAPR International Workshop on Structural, Syntactic, and Statistical Pattern Recognition
Biologically Inspired Architecture of Feedforward Networks for Signal Classification
Proceedings of the Joint IAPR International Workshops on Advances in Pattern Recognition
Data Complexity Analysis for Classifier Combination
MCS '01 Proceedings of the Second International Workshop on Multiple Classifier Systems
An Experimental Comparison of Fixed and Trained Fusion Rules for Crisp Classifier Outputs
MCS '02 Proceedings of the Third International Workshop on Multiple Classifier Systems
Reduction of the Boasting Bias of Linear Experts
MCS '02 Proceedings of the Third International Workshop on Multiple Classifier Systems
Experts' Boasting in Trainable Fusion Rules
IEEE Transactions on Pattern Analysis and Machine Intelligence
Experts' Boasting in Trainable Fusion Rules
IEEE Transactions on Pattern Analysis and Machine Intelligence
Trainable fusion rules. I. Large sample size case
Neural Networks
Reducing the overconfidence of base classifiers when combining their decisions
MCS'03 Proceedings of the 4th international conference on Multiple classifier systems
A pool of classifiers by SLP: a multi-class case
ICIAR'06 Proceedings of the Third international conference on Image Analysis and Recognition - Volume Part II
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Parallels between Feature Extraction / Selection and Multiple Classification Systems methodologies are considered. Both approaches allow the designer to introduce prior information about the pattern recognition task to be solved. However, both are heavily affected by computational difficulties and by the problem of small sample size / classifier complexity. Neither approach is capable of selecting a unique data analysis algorithm.