In this paper a dissimilarity index between statistical populations is pro-posed without the hypothesis of a specific statistical model. We assume that the studied populations differ on some relevant features which are measured trough convenient parameters of interest. We assume also that we dispose of adequate estimators for these parameters. To measure the differences be-tween populations with respect the parameters of interest, we construct an index inspired on some properties of the information metric which are also presented. Additionally, we consider several examples and compare the ob-tained dissimilarity index with some other distances, like Mahalanobis or Siegel distances
AbstractThe degree of segregation between two or more sub-populations has been studied since the 195...
International audienceThis paper deals with the problem of measuring the similarity degree between t...
Abstract—Nearest neighbour search is a core process in many data mining algorithms. Finding reliable...
In this paper a dissimilarity index between statistical populations is pro-posed without the hypothe...
The power transformation that turns an arbitrary even dissimilarity into a semidistance or a definit...
AbstractThe purpose of the present work is to survey the dissimilarity measures defined so far in th...
In many real-world applications concerning pattern recognition techniques, it is of utmost importanc...
We discuss definitions and properties of similarity and dissimilarity coefficients, including their ...
Copyright © 2013 Giovanni Girone, Antonella Nannavecchia. This is an open access article distributed...
This paper presents a dissimilarity-based discriminative framework for learning from data coming in...
In the last decade there has been an increasing interest in mining time series data and many distanc...
TEZ10860Tez (Yüksek Lisans) -- Çukurova Üniversitesi, Adana, 2015.Kaynakça (s. 89-96) var.viii, 97 s...
The degree of segregation between two or more sub-populations has been studied since the 1950s, and ...
Abstract— In image retrieval, an effective dissimilarity measure is required to retrieve the percept...
Statistical distances allow us to quantify the closeness between two statistical objects. Many dista...
AbstractThe degree of segregation between two or more sub-populations has been studied since the 195...
International audienceThis paper deals with the problem of measuring the similarity degree between t...
Abstract—Nearest neighbour search is a core process in many data mining algorithms. Finding reliable...
In this paper a dissimilarity index between statistical populations is pro-posed without the hypothe...
The power transformation that turns an arbitrary even dissimilarity into a semidistance or a definit...
AbstractThe purpose of the present work is to survey the dissimilarity measures defined so far in th...
In many real-world applications concerning pattern recognition techniques, it is of utmost importanc...
We discuss definitions and properties of similarity and dissimilarity coefficients, including their ...
Copyright © 2013 Giovanni Girone, Antonella Nannavecchia. This is an open access article distributed...
This paper presents a dissimilarity-based discriminative framework for learning from data coming in...
In the last decade there has been an increasing interest in mining time series data and many distanc...
TEZ10860Tez (Yüksek Lisans) -- Çukurova Üniversitesi, Adana, 2015.Kaynakça (s. 89-96) var.viii, 97 s...
The degree of segregation between two or more sub-populations has been studied since the 1950s, and ...
Abstract— In image retrieval, an effective dissimilarity measure is required to retrieve the percept...
Statistical distances allow us to quantify the closeness between two statistical objects. Many dista...
AbstractThe degree of segregation between two or more sub-populations has been studied since the 195...
International audienceThis paper deals with the problem of measuring the similarity degree between t...
Abstract—Nearest neighbour search is a core process in many data mining algorithms. Finding reliable...