Add to ORKGLinear Discriminant Analysis (LDA) performs well for classifica-tion of business phases – even though the premises of an LDA are not met. As the variables are highly correlated there are numerical as well as interpretational shortcomings. By transforming the classification problem to a regression setting both problems can be addressed by a computer-intensive prediction oriented method which also improves the classification performance.
Linear discriminant analysis (LDA) as a dimension reduction method is widely used in data mining and...
In psychology, linear discriminant analysis (LDA) is the method of choice for two-group classificati...
Linear discriminant analysis (LDA) as a dimension reduction method is widely used in data mining and...
Linear Discriminant Analysis (LDA) performs well for classification of business phases – even though...
Linear Discriminant Analysis (LDA) performs well for classifica- tion of business phases – even thou...
The proliferation of online platforms recently has led to unprecedented increase in data generation;...
Linear discriminant analysis (LDA) is a part of classification methods that has been widely used in ...
Discriminant analysis (DA) is a descriptive multivariate technique for analyzing grouped data, i.e. ...
We analyzed the performance of LDA, Quadratic Discriminant Analysis (QDA), K-Nearest Neighbours (KNN...
Two of the most widely used statistical methods for analyzing categorical outcome variables are line...
This paper discusses the strategy of conducting variable reduction processes such that they contribu...
Fisher--Rao Linear Discriminant Analysis (LDA), a valuable tool for multigroup classification and da...
Principal Components Analysis (PCA) is a variable reduction technique helps to reduce a complex data...
Abstract — In the so-called high dimensional, low sample size (HDLSS) settings, LDA possesses the “d...
This study compares the classification accuracy of linear discriminant analysis (LDA), quadratic dis...
Linear discriminant analysis (LDA) as a dimension reduction method is widely used in data mining and...
In psychology, linear discriminant analysis (LDA) is the method of choice for two-group classificati...
Linear discriminant analysis (LDA) as a dimension reduction method is widely used in data mining and...
Linear Discriminant Analysis (LDA) performs well for classification of business phases – even though...
Linear Discriminant Analysis (LDA) performs well for classifica- tion of business phases – even thou...
The proliferation of online platforms recently has led to unprecedented increase in data generation;...
Linear discriminant analysis (LDA) is a part of classification methods that has been widely used in ...
Discriminant analysis (DA) is a descriptive multivariate technique for analyzing grouped data, i.e. ...
We analyzed the performance of LDA, Quadratic Discriminant Analysis (QDA), K-Nearest Neighbours (KNN...
Two of the most widely used statistical methods for analyzing categorical outcome variables are line...
This paper discusses the strategy of conducting variable reduction processes such that they contribu...
Fisher--Rao Linear Discriminant Analysis (LDA), a valuable tool for multigroup classification and da...
Principal Components Analysis (PCA) is a variable reduction technique helps to reduce a complex data...
Abstract — In the so-called high dimensional, low sample size (HDLSS) settings, LDA possesses the “d...
This study compares the classification accuracy of linear discriminant analysis (LDA), quadratic dis...
Linear discriminant analysis (LDA) as a dimension reduction method is widely used in data mining and...
In psychology, linear discriminant analysis (LDA) is the method of choice for two-group classificati...
Linear discriminant analysis (LDA) as a dimension reduction method is widely used in data mining and...