<p>Data are mean (SD); <i>P</i> (e<sub>act</sub>), <i>P</i> (sen<sub>act</sub>) and <i>P</i> (spec<sub>act</sub>) representing the mean <i>p</i>-value of the permutation test for the actual classification error, sensitivity and specificity.</p
A computationally efficient approach has been developed to perform two-group linear discriminant ana...
Discriminant analysis is a multivariate statistical technique used primarily for obtaining a linear ...
When the population, from which the samples are extracted, is not normally distributed, or if the sa...
Abstract. We investigate the problem of supervised feature selection within the filtering framework....
Abstract We explore the framework of permutation-based p-values for assessing the performance of cla...
We introduce and explore an approach to estimating statisticalsignificance of classification accurac...
<p>Class prediction results for NMR profiles based on test set predictions of the original labelling...
<p>(Coef: coefficient from linear regression, P: P value from permutation likelyhood ratio test for ...
Editor: Permutation tests have been proposed for a variety of problems going back to the early works...
Python implementation of Fisher's permutation test, with an extension for error propagation. A deta...
The estimation of mutual information for feature selection is often subject to inaccuracies due to n...
The coefficients of the classification function include Age Group, Sex and Gender, and Occupation. T...
<p>Classification accuracy from permutation Discriminant Function Analysis based on the viewing time...
<p>(A) Each gray bar reports the average performance of a binary FLD at correctly classifying member...
Abstract Investigating a data set of the critical size makes a classifica-tion task difficult. Study...
A computationally efficient approach has been developed to perform two-group linear discriminant ana...
Discriminant analysis is a multivariate statistical technique used primarily for obtaining a linear ...
When the population, from which the samples are extracted, is not normally distributed, or if the sa...
Abstract. We investigate the problem of supervised feature selection within the filtering framework....
Abstract We explore the framework of permutation-based p-values for assessing the performance of cla...
We introduce and explore an approach to estimating statisticalsignificance of classification accurac...
<p>Class prediction results for NMR profiles based on test set predictions of the original labelling...
<p>(Coef: coefficient from linear regression, P: P value from permutation likelyhood ratio test for ...
Editor: Permutation tests have been proposed for a variety of problems going back to the early works...
Python implementation of Fisher's permutation test, with an extension for error propagation. A deta...
The estimation of mutual information for feature selection is often subject to inaccuracies due to n...
The coefficients of the classification function include Age Group, Sex and Gender, and Occupation. T...
<p>Classification accuracy from permutation Discriminant Function Analysis based on the viewing time...
<p>(A) Each gray bar reports the average performance of a binary FLD at correctly classifying member...
Abstract Investigating a data set of the critical size makes a classifica-tion task difficult. Study...
A computationally efficient approach has been developed to perform two-group linear discriminant ana...
Discriminant analysis is a multivariate statistical technique used primarily for obtaining a linear ...
When the population, from which the samples are extracted, is not normally distributed, or if the sa...
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