Journal ArticleDOI
Expected classification error of the Fisher linear classifier with pseudo-inverse covariance matrix
Sarunas Raudys,Robert P. W. Duin +1 more
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TLDR
An asymptotic formula for the expected (generalization) error of the Fisher classifier with the pseudo-inversion is derived which explains the peaking behaviour: with an increasing number of learning observations from one up to the number of features, the generalization error first decreases, and then starts to increase.About:
This article is published in Pattern Recognition Letters.The article was published on 1998-04-01. It has received 207 citations till now. The article focuses on the topics: Margin classifier & Quadratic classifier.read more
Citations
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Journal ArticleDOI
Statistical pattern recognition: a review
TL;DR: The objective of this review paper is to summarize and compare some of the well-known methods used in various stages of a pattern recognition system and identify research topics and applications which are at the forefront of this exciting and challenging field.
Journal ArticleDOI
Single-Trial Analysis and Classification of ERP Components - a Tutorial
TL;DR: This tutorial proposes to use shrinkage estimators and shows that appropriate regularization of linear discriminant analysis (LDA) by shrinkage yields excellent results for single-trial ERP classification that are far superior to classical LDA classification.
Decision templates for multiple classi"er fusion: an experimental comparison
TL;DR: This work presents here a simple rule for adapting the class combiner to the application and shows that decision templates based on integral type measures of similarity are superior to the other schemes on both data sets.
Journal ArticleDOI
Decision templates for multiple classifier fusion: an experimental comparison.
TL;DR: In this article, a simple rule for adapting the class combiner to the application is presented, where decision templates (one per class) are estimated with the same training set that is used for the set of classifiers.
Journal ArticleDOI
An empirical Bayes approach to inferring large-scale gene association networks
TL;DR: A novel framework for small-sample inference of graphical models from gene expression data that focuses on the so-called graphical Gaussian models (GGMs) that are now frequently used to describe gene association networks and to detect conditionally dependent genes is introduced.
References
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Book
Introduction to Statistical Pattern Recognition
TL;DR: This completely revised second edition presents an introduction to statistical pattern recognition, which is appropriate as a text for introductory courses in pattern recognition and as a reference book for workers in the field.
Book
An Introduction to Multivariate Statistical Analysis
TL;DR: In this article, the distribution of the Mean Vector and the Covariance Matrix and the Generalized T2-Statistic is analyzed. But the distribution is not shown to be independent of sets of Variates.
Journal ArticleDOI
Introduction to Multivariate Statistical Analysis.
William G. Madow,T. W. Anderson +1 more
Book
Discriminant Analysis and Statistical Pattern Recognition
TL;DR: In this article, the authors provide a systematic account of the subject area, concentrating on the most recent advances in the field and discuss theoretical and practical issues in statistical image analysis, including regularized discriminant analysis and bootstrap-based assessment of the performance of a sample-based discriminant rule.