Introduction to statistical pattern recognition (2nd ed.)
Introduction to statistical pattern recognition (2nd ed.)
Machine Learning
Machine Learning
The Random Subspace Method for Constructing Decision Forests
IEEE Transactions on Pattern Analysis and Machine Intelligence
Expected classification error of the Fisher linear classifier with pseudo-inverse covariance matrix
Pattern Recognition Letters
Relational discriminant analysis
Pattern Recognition Letters - Special issue on pattern recognition in practice VI
Nearest Neighbors in Random Subspaces
SSPR '98/SPR '98 Proceedings of the Joint IAPR International Workshops on Advances in Pattern Recognition
Boosting in Linear Discriminant Analysis
MCS '00 Proceedings of the First International Workshop on Multiple Classifier Systems
Forensic Authorship Attribution Using Compression Distances to Prototypes
IWCF '09 Proceedings of the 3rd International Workshop on Computational Forensics
Heat diffusion based dissimilarity analysis for schizophrenia classification
PRIB'11 Proceedings of the 6th IAPR international conference on Pattern recognition in bioinformatics
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Investigating a data set of the critical size makes a classification task difficult. Studying dissimilarity data refers to such a problem, since the number of samples equals their dimensionality. In such a case, a simple classifier is expected to generalize better than the complex one. Earlier experiments [9,3] confirm that in fact linear decision rules perform reasonably well on dissimilarity representations. For the Pseudo-Fisher linear discriminant the situation considered is the most inconvenient since the generalization error approaches its maximum when the size of a learning set equals the dimensionality [10]. However, some improvement is still possible. Combined classifiers may handle this problem better when a more powerful decision rule is found. In this paper, the usefulness of bagging and boosting of the Fisher linear discriminant for dissimilarity data is discussed and a newmetho d based on random subspaces is proposed. This technique yields only a single linear pattern recognizer in the end and still significantly improves the accuracy.