Original Contribution: Stacked generalization
Neural Networks
Decision Combination in Multiple Classifier Systems
IEEE Transactions on Pattern Analysis and Machine Intelligence
A Method of Combining Multiple Experts for the Recognition of Unconstrained Handwritten Numerals
IEEE Transactions on Pattern Analysis and Machine Intelligence
An alternative method of stochastic discrimination with applications to pattern recognition
An alternative method of stochastic discrimination with applications to pattern recognition
Machine Learning
IEEE Transactions on Pattern Analysis and Machine Intelligence
The Random Subspace Method for Constructing Decision Forests
IEEE Transactions on Pattern Analysis and Machine Intelligence
On the Algorithmic Implementation of Stochastic Discrimination
IEEE Transactions on Pattern Analysis and Machine Intelligence
IEEE Transactions on Pattern Analysis and Machine Intelligence
Nearest Neighbors in Random Subspaces
SSPR '98/SPR '98 Proceedings of the Joint IAPR International Workshops on Advances in Pattern Recognition
A Mathematically Rigorous Foundation for Supervised Learning
MCS '00 Proceedings of the First International Workshop on Multiple Classifier Systems
ICDAR '95 Proceedings of the Third International Conference on Document Analysis and Recognition (Volume 1) - Volume 1
Estimates of classification accuracies for kleinberg's method of stochastic discrimination in pattern recognition
Estimation of Dependences Based on Empirical Data: Springer Series in Statistics (Springer Series in Statistics)
Building Projectable Classifiers of Arbitrary Complexity
ICPR '96 Proceedings of the 13th International Conference on Pattern Recognition - Volume 2
Solving multiclass learning problems via error-correcting output codes
Journal of Artificial Intelligence Research
Application of majority voting to pattern recognition: an analysis of its behavior and performance
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
SCS: Signal, Context, and Structure Features for Genome-Wide Human Promoter Recognition
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
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Studies on ensemble methods for classification suffer from the difficulty of modeling the complementary strengths of the components. Kleinberg’s theory of stochastic discrimination (SD) addresses this rigorously via mathematical notions of enrichment, uniformity, and projectability of a model ensemble. We explain these concepts via a very simple numerical example that captures the basic principles of the SD theory and method. We focus on a fundamental symmetry in point set covering that is the key observation leading to the foundation of the theory. We believe a better understanding of the SD method will lead to developments of better tools for analyzing other ensemble methods.