Machine Learning
On Optimal Pairwise Linear Classifiers for Normal Distributions: The Two-Dimensional Case
IEEE Transactions on Pattern Analysis and Machine Intelligence
Randomized Algorithms: A System-Level, Poly-Time Analysis of Robust Computation
IEEE Transactions on Computers
An approach to the evaluation of the performance of a discrete classifier
Pattern Recognition Letters
The Foundational Theory of Optimal Bayesian Pairwise Linear Classifiers
Proceedings of the Joint IAPR International Workshops on Advances in Pattern Recognition
Complexity of Classification Problems and Comparative Advantages of Combined Classifiers
MCS '00 Proceedings of the First International Workshop on Multiple Classifier Systems
The d-Dimensional Normal Distribution Case
AI '01 Proceedings of the 14th Australian Joint Conference on Artificial Intelligence: Advances in Artificial Intelligence
The influence of prior knowledge on the expected performance of a classifier
Pattern Recognition Letters
Selecting the best hyperplane in the framework of optimal pairwise linear classifiers
Pattern Recognition Letters
Results in statistical discriminant analysis: a review of the former Soviet union literature
Journal of Multivariate Analysis
On the Bayes fusion of visual features
Image and Vision Computing
Classification tree based protein structure distances for testing sequence-structure correlation
Computers in Biology and Medicine
eigenPulse: Robust human identification from cardiovascular function
Pattern Recognition
IJCNN'09 Proceedings of the 2009 international joint conference on Neural Networks
Computers in Biology and Medicine
Invariant operators, small samples, and the bias-variance dilemma
CVPR'04 Proceedings of the 2004 IEEE computer society conference on Computer vision and pattern recognition
MMES'10 Proceedings of the 2010 international conference on Mathematical models for engineering science
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
Alternative approaches and algorithms for classification
ICIAR'06 Proceedings of the Third international conference on Image Analysis and Recognition - Volume Part II
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
Computational Statistics & Data Analysis
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This paper compares two nonparametric linear classification algorithms驴the zero empirical error classifier and the maximum margin classifier驴with parametric linear classifiers designed to classify multivariate Gaussian populations [7]. Formulae and a table for the mean expected probability of misclassification MEPN are presented. They show that the classification error is mainly determined by N驴/驴p, a learning-set size/dimensionality ratio. However, the influences of learning-set size on the generalization error of parametric and nonparametric linear classifiers are quite different. Under certain conditions the nonparametric approach allows us to obtain reliable rules, even in cases where the number of features is larger than the number of training vectors.