Journal ArticleDOI
Bias of Apparent Error Rate in Discriminant-Analysis
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This article is published in Biometrika.The article was published on 1976-01-01. It has received 9 citations till now. The article focuses on the topics: Linear discriminant analysis & Word error rate.read more
Citations
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Book
A Probabilistic Theory of Pattern Recognition
TL;DR: The Bayes Error and Vapnik-Chervonenkis theory are applied as guide for empirical classifier selection on the basis of explicit specification and explicit enforcement of the maximum likelihood principle.
Wrappers for Performance Enhancements and Oblivious Decision Graphs.
TL;DR: This doctoral dissertation concludes that repeated runs of five-fold cross-validation give a good tradeoff between bias and variance for the problem of model selection used in later chapters.
Journal ArticleDOI
Automatic pattern recognition: a study of the probability of error
TL;DR: The Vapnik-Chervonenkis method can be used to choose the smoothing parameter in kernel-based rules, to choose k in the k-nearest neighbor rule, and to choose between parametric and nonparametric rules.
Journal ArticleDOI
An automated method to analyze language use in patients with schizophrenia and their first-degree relatives
TL;DR: An automated and objective approach to modeling discourse that detects very subtle deviations between probands, their first-degree relatives and unrelated healthy controls is presented.
Journal ArticleDOI
Model selection for linear classifiers using Bayesian error estimation
Heikki Huttunen,Jussi Tohka +1 more
TL;DR: The model selection by the new Bayesian error estimator is experimentally shown to improve the classification accuracy, especially in small sample-size situations, and is able to avoid the excess variability inherent to traditional cross-validation approaches.
References
More filters
Book
A Probabilistic Theory of Pattern Recognition
TL;DR: The Bayes Error and Vapnik-Chervonenkis theory are applied as guide for empirical classifier selection on the basis of explicit specification and explicit enforcement of the maximum likelihood principle.
Wrappers for Performance Enhancements and Oblivious Decision Graphs.
TL;DR: This doctoral dissertation concludes that repeated runs of five-fold cross-validation give a good tradeoff between bias and variance for the problem of model selection used in later chapters.
Journal ArticleDOI
Automatic pattern recognition: a study of the probability of error
TL;DR: The Vapnik-Chervonenkis method can be used to choose the smoothing parameter in kernel-based rules, to choose k in the k-nearest neighbor rule, and to choose between parametric and nonparametric rules.
Journal ArticleDOI
An automated method to analyze language use in patients with schizophrenia and their first-degree relatives
TL;DR: An automated and objective approach to modeling discourse that detects very subtle deviations between probands, their first-degree relatives and unrelated healthy controls is presented.
Journal ArticleDOI
Model selection for linear classifiers using Bayesian error estimation
Heikki Huttunen,Jussi Tohka +1 more
TL;DR: The model selection by the new Bayesian error estimator is experimentally shown to improve the classification accuracy, especially in small sample-size situations, and is able to avoid the excess variability inherent to traditional cross-validation approaches.