Introduction to statistical pattern recognition (2nd ed.)
Introduction to statistical pattern recognition (2nd ed.)
Pattern recognition: statistical, structural and neural approaches
Pattern recognition: statistical, structural and neural approaches
Linear Discriminant Analysis for Two Classes via Removal of Classification Structure
IEEE Transactions on Pattern Analysis and Machine Intelligence
IEEE Transactions on Pattern Analysis and Machine Intelligence
A Deterministic Annealing Approach for Parsimonious Design of Piecewise Regression Models
IEEE Transactions on Pattern Analysis and Machine Intelligence
Two Variations on Fisher's Linear Discriminant for Pattern Recognition
IEEE Transactions on Pattern Analysis and Machine Intelligence
On Optimal Pairwise Linear Classifiers for Normal Distributions: The Two-Dimensional Case
IEEE Transactions on Pattern Analysis and Machine Intelligence
AI Game Programming Wisdom
Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
Chernoff-Based Multi-class Pairwise Linear Dimensionality Reduction
CIARP '08 Proceedings of the 13th Iberoamerican congress on Pattern Recognition: Progress in Pattern Recognition, Image Analysis and Applications
A theoretical comparison of two linear dimensionality reduction techniques
CIARP'06 Proceedings of the 11th Iberoamerican conference on Progress in Pattern Recognition, Image Analysis and Applications
A new approach to multi-class linear dimensionality reduction
CIARP'06 Proceedings of the 11th Iberoamerican conference on Progress in Pattern Recognition, Image Analysis and Applications
A new linear dimensionality reduction technique based on chernoff distance
IBERAMIA-SBIA'06 Proceedings of the 2nd international joint conference, and Proceedings of the 10th Ibero-American Conference on AI 18th Brazilian conference on Advances in Artificial Intelligence
On the performance of chernoff-distance-based linear dimensionality reduction techniques
AI'06 Proceedings of the 19th international conference on Advances in Artificial Intelligence: Canadian Society for Computational Studies of Intelligence
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In this paper, we introduce a new approach to selecting the best hyperplane from the pairwise classifier (BHPC) when the optimal pairwise linear classifier is given. We first propose a procedure for selecting the BHPC, and analyze the conditions in which the BHPC is selected. In one of the cases, it is formally shown that the BHPC and Fisher's classifier (FC) are coincident. To evaluate the efficiency of the new classifier, we present an empirical and graphical analysis on synthetic data and real-life datasets from the UCI machine learning repository, which involves the optimal quadratic classifier, the BHPC, the optimal pairwise linear classifier, and FC. A numerical analysis of the classification error for these classifiers is also included. The results obtained demonstrate that the BHPC is more accurate than FC, and achieves nearly optimal classification.