Add to ORKGThe problem of reducing the dimensionality in statistical classification is studied. The case of the well-known Fisher model of multivariate normal (Gaussian) distribution mixture is considered. The average decrease of interclass distances square is presented as a new criterion of feature selection directly connected with the classification error probability. The stepwise discriminant analysis procedure based on this criterion is propose
Discriminant analysis is a multivariate statistical technique used primarily for obtaining a linear ...
Given a classification problem, our goal is to find a low-dimensional linear transformation of the f...
In classification, a large number of features often make the design of a classifier difficult and de...
The problem of reducing the dimensionality in statistical classification is studied. The case of the...
In the multivariate single classification or one way analysis of variance model the mean vectors of ...
Dimension reduction transformations in discriminant analysis are introduced. Their properties, as we...
Dimension reduction transformations in discriminant analysis are introduced. Their properties, as we...
Dimension reduction transformations in discriminant analysis are introduced. Their properties, as we...
Dimension reduction transformations in discriminant analysis are introduced. Their properties, as we...
Dimension reduction transformations in discriminant analysis are introduced. Their properties, as we...
The problem of statistical classification of multivariate normal (Gaussian) observations in the subs...
The problem of statistical classification of multivariate normal (Gaussian) observations in the subs...
Abstract Investigating a data set of the critical size makes a classifica-tion task difficult. Study...
A new version of Fisher's discriminant analysis (FDA) is introduced in this paper. Our algorithm sea...
The following thesis studies both parametric and non parametric approaches to classification. Among ...
Discriminant analysis is a multivariate statistical technique used primarily for obtaining a linear ...
Given a classification problem, our goal is to find a low-dimensional linear transformation of the f...
In classification, a large number of features often make the design of a classifier difficult and de...
The problem of reducing the dimensionality in statistical classification is studied. The case of the...
In the multivariate single classification or one way analysis of variance model the mean vectors of ...
Dimension reduction transformations in discriminant analysis are introduced. Their properties, as we...
Dimension reduction transformations in discriminant analysis are introduced. Their properties, as we...
Dimension reduction transformations in discriminant analysis are introduced. Their properties, as we...
Dimension reduction transformations in discriminant analysis are introduced. Their properties, as we...
Dimension reduction transformations in discriminant analysis are introduced. Their properties, as we...
The problem of statistical classification of multivariate normal (Gaussian) observations in the subs...
The problem of statistical classification of multivariate normal (Gaussian) observations in the subs...
Abstract Investigating a data set of the critical size makes a classifica-tion task difficult. Study...
A new version of Fisher's discriminant analysis (FDA) is introduced in this paper. Our algorithm sea...
The following thesis studies both parametric and non parametric approaches to classification. Among ...
Discriminant analysis is a multivariate statistical technique used primarily for obtaining a linear ...
Given a classification problem, our goal is to find a low-dimensional linear transformation of the f...
In classification, a large number of features often make the design of a classifier difficult and de...