observations and features for classification. D-optimal plans are searched for in existing data and based on these plans the … variables most relevant for classification are determined. The adapted models are then compared with respect to their predictive …

observations and features for classification. D-optimal plans are searched for in existing data and based on these plans the … variables most relevant for classification are determined. The adapted models are then compared with respect to their predictive …

lemma is outlined and an application of such a projection on latent factors in a classification example is given. …

multivariate classification. …

We propose a computer intensive method for linear dimension reduction which minimizes the classification error directly … optimal classification to classical discriminant analysis (McLachlan 1992). Special emphasis is put on the case when …

We propose multivariate classification as a statistical tool to describe business cycles. These cycles are often … economic variables. Multivariate classification overcomes these limitations by reducing dimension in a way suitable for human … potential of classification methods by determining the important economic variables (stylized facts) for the German business …

of classification rules. Using SPS, one can define measures for the performance of classifiers w.r.t. goodness concepts …, we are interested in assessing the reliability of classification rules when used for proceeding interpretation of the … comparison of classification methods in a simulation study to analyse the following problem: Given a medium number of predictors …

We propose a computer intensive method for linear dimension reduction which minimizes the classification error directly … optimal classification to classical discriminant analysis (McLachlan 1992). Special emphasis is put on the case when …