Preprint enviat per a la seva publicació en una revista científica.A general distance based method for allocating an observation to one of several known populations, on the ha.sis of both continuous and discrete explanatory variables, is proposed and studied. This method depends on a given statistical distance between observations, and leads to sorne classic discriminant rules by taking suitable distances. The error rates can be easily computed and, unlike other distance classification rules, the prior probabilities can be taken into account
Cluster and discriminant analysis belong to basic classification methods. Using cluster analysis can...
International audienceStatistical pattern recognition traditionally relies on features-based represe...
The ultimate goal of distance metric learning is to incorporate abundant discriminative information ...
National audienceStatistical pattern recognition traditionally relies on a features based representa...
El concepte de distància s'ha utilizat en diferents camps i és bàsic en alguns mètodes estadístics r...
AbstractIt is shown that for the two-group case, classification by minimum distance is equivalent to...
Building on probabilistic models for interval-valued variables, parametric classification rules, bas...
We describe a principled way of imposing a metric representing dissimilarities on any discrete set o...
The WeDiBaDis package provides a user friendly environment to perform discriminant analysis (supervi...
This package implements the generalized distance-based linear discriminant analysis method and one c...
The problem of specifying an individual as a member of one of many populations, and the classificati...
This paper proposes a method of finding a discriminative linear transformation that enhances the dat...
Treballs Finals de Grau de Matemàtiques, Facultat de Matemàtiques, Universitat de Barcelona, Any: 20...
Most multivariate tests are based on the hypothesis of multinormality. But often this hypothesis fai...
Traditional discriminate analysis treats all the involved classes equally in the computation of the ...
Cluster and discriminant analysis belong to basic classification methods. Using cluster analysis can...
International audienceStatistical pattern recognition traditionally relies on features-based represe...
The ultimate goal of distance metric learning is to incorporate abundant discriminative information ...
National audienceStatistical pattern recognition traditionally relies on a features based representa...
El concepte de distància s'ha utilizat en diferents camps i és bàsic en alguns mètodes estadístics r...
AbstractIt is shown that for the two-group case, classification by minimum distance is equivalent to...
Building on probabilistic models for interval-valued variables, parametric classification rules, bas...
We describe a principled way of imposing a metric representing dissimilarities on any discrete set o...
The WeDiBaDis package provides a user friendly environment to perform discriminant analysis (supervi...
This package implements the generalized distance-based linear discriminant analysis method and one c...
The problem of specifying an individual as a member of one of many populations, and the classificati...
This paper proposes a method of finding a discriminative linear transformation that enhances the dat...
Treballs Finals de Grau de Matemàtiques, Facultat de Matemàtiques, Universitat de Barcelona, Any: 20...
Most multivariate tests are based on the hypothesis of multinormality. But often this hypothesis fai...
Traditional discriminate analysis treats all the involved classes equally in the computation of the ...
Cluster and discriminant analysis belong to basic classification methods. Using cluster analysis can...
International audienceStatistical pattern recognition traditionally relies on features-based represe...
The ultimate goal of distance metric learning is to incorporate abundant discriminative information ...