Applied multivariate statistical analysis
Applied multivariate statistical analysis
An alternative method of stochastic discrimination with applications to pattern recognition
An alternative method of stochastic discrimination with applications to pattern recognition
The Random Subspace Method for Constructing Decision Forests
IEEE Transactions on Pattern Analysis and Machine Intelligence
Pattern Recognition and Neural Networks
Pattern Recognition and Neural Networks
ICDAR '95 Proceedings of the Third International Conference on Document Analysis and Recognition (Volume 1) - Volume 1
Building Projectable Classifiers of Arbitrary Complexity
ICPR '96 Proceedings of the 13th International Conference on Pattern Recognition - Volume 2
MCS '01 Proceedings of the Second International Workshop on Multiple Classifier Systems
A novel training weighted ensemble (TWE) with application to face recognition
Applied Soft Computing
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The method of stochastic discrimination (SD) introduced by Kleinberg ([6,7])is a new method in pattern recognition. It works by producing weak classifiers and then combining them via the Central Limit Theorem to form a strong classifier. SD is overtraining-resistant, has a high convergence rate, and can work quite well in practice. However, some strict assumptions involved in SD and the difficulties in understanding SD have limited its practical use. In this paper, we present a simple algorithm of SD for two-class pattern recognition. We illustrate the algorithm by applications in classifying the feature vectors from some real and simulated data sets. The experimental results show that SD is fast, effective, and applicable.