Journal ArticleDOI
A comparison of asymptotic error rate expansions for the sample linear discriminant function
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TLDR
A simple and relatively obscure asymPTotic expansion derived by Raudys is found to yield better approximation than the well-known asymptotic expansions.About:
This article is published in Pattern Recognition.The article was published on 1990-07-20. It has received 51 citations till now. The article focuses on the topics: Asymptotic analysis & Method of matched asymptotic expansions.read more
Citations
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Expected classification error of the Fisher linear classifier with pseudo-inverse covariance matrix
Sarunas Raudys,Robert P. W. Duin +1 more
TL;DR: 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.
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Valid population inference for information-based imaging: From the second-level t-test to prevalence inference.
TL;DR: It is argued that while the random-effects analysis implemented by the t-test does provide population inference if applied to activation differences, it fails to do so in the case of classification accuracy or other 'information-like' measures, because the true value of such measures can never be below chance level.
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Searchlight-based multi-voxel pattern analysis of fMRI by cross-validated MANOVA.
TL;DR: This work proposes to replace the standard 'decoding' approach to searchlight-based MVPA, measuring the performance of a classifier by its accuracy, with a method based on the multivariate form of the general linear model, making the full analytical power of complex factorial designs known from univariate fMRI analyses available to MVPA studies.
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Results in statistical discriminant analysis: a review of the former Soviet union literature
Šarūnas Raudys,Dean M. Young +1 more
TL;DR: In this paper, a succinct overview of important contributions by former Soviet Block researchers to several topics in the discriminant analysis literature concerning the small training-sample size problem is given. But most results derived by former former Soviet Union researchers are unknown to statisticians and statistical pattern recognition researchers in the West.
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On dimensionality, sample size, and classification error of nonparametric linear classification algorithms
TL;DR: Two nonparametric linear classification algorithms - the zero empirical error classifier and the maximum margin classifier - with parametric linear classifiers designed to classify multivariate Gaussian populations are compared.
References
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Journal ArticleDOI
The generalisation of student's problems when several different population variances are involved.
Journal ArticleDOI
The Efficiency of Logistic Regression Compared to Normal Discriminant Analysis
TL;DR: In this article, the asymptotic relative efficiency of the normal discrimination procedure and logistic regression is compared, and it is shown that the latter procedure is between one half and two thirds as effective as normal discrimination for statistically interesting values of the parameters.
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An Asymptotic Expansion for the Distribution of the Linear Discriminant Function
TL;DR: In this paper, an asymptotic expansion of the distribution with respect to three numbers $N_1, N_2$ and $n$ representing degrees of freedom is presented.
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Classification by multivariate analysis
TL;DR: In this paper, the problem of using a set of measurements on an individual to decide from which of several populations he has been drawn is considered, and the principles for choosing the rule of classification are based on costs of misclassification.
Related Papers (5)
Small sample size effects in statistical pattern recognition: recommendations for practitioners
Sarunas Raudys,Anil K. Jain +1 more