Bayesian networks are directed acyclic graphs that code the relationships of conditional dependence and independence in a set of predictive variables. In this research work, three algorithms that allow to obtain the structure that defines a bayesian network are presented. Classifiers were built on this structure, including a dependent variable in the graph that has the classes or categories of interest, obtaining a similar predictive performance compared to the classifiers by traditional bayesian networks, Naive Bayes and TAN. The Statistically Equivalent Signature variable selection algorithm is also presented, obtaining similar results to the classifiers constructed with all the predictive variables.Las redes bayesianas son gráfico...
Bayesian networks are directed acyclic graphs representing independence relationships among a set of...
Cada vez son más numerosas las investigaciones que trabajan con un amplio número de variables donde ...
The paper gives a few arguments in favour of use of chain graphs for description of probabilistic co...
Las redes bayesianas y, en general, los modelos gráficos probabilísticos constan de un grafo que rec...
One of the most recent knowledge representations under uncertainty are Bayesian Networks whose main ...
One of the most recent knowledge representations under uncertainty are Bayesian Networks whose main ...
A Bayesian network is a graph which features conditional probability tables as edges, and variabl...
Los modelos explicables son aquellos que necesitan de otro modelo u otras técnicas para entender las...
En las últimas décadas, el aprendizaje automático ha adquirido importancia como una de las herramien...
En este trabajo introduciremos los conceptos y contenidos probabilísticos sobre los que se fundament...
Este trabalho é uma investigação sobre o comportamento das Redes Bayesianas (RB) discretas que visam...
Esta tesis está centrada en el campo de los modelos gráficos probabilísticos. En ella se desarrollan...
Includes bibliographical references (page 48).San Diego State University copy: the accompanying CD-R...
The structure of a Bayesian network (BN) encodes variable independence. Learning the structure of a ...
An analysis of Bayesian networks as classifiers is presented. This analysis results in an algorithm ...
Bayesian networks are directed acyclic graphs representing independence relationships among a set of...
Cada vez son más numerosas las investigaciones que trabajan con un amplio número de variables donde ...
The paper gives a few arguments in favour of use of chain graphs for description of probabilistic co...
Las redes bayesianas y, en general, los modelos gráficos probabilísticos constan de un grafo que rec...
One of the most recent knowledge representations under uncertainty are Bayesian Networks whose main ...
One of the most recent knowledge representations under uncertainty are Bayesian Networks whose main ...
A Bayesian network is a graph which features conditional probability tables as edges, and variabl...
Los modelos explicables son aquellos que necesitan de otro modelo u otras técnicas para entender las...
En las últimas décadas, el aprendizaje automático ha adquirido importancia como una de las herramien...
En este trabajo introduciremos los conceptos y contenidos probabilísticos sobre los que se fundament...
Este trabalho é uma investigação sobre o comportamento das Redes Bayesianas (RB) discretas que visam...
Esta tesis está centrada en el campo de los modelos gráficos probabilísticos. En ella se desarrollan...
Includes bibliographical references (page 48).San Diego State University copy: the accompanying CD-R...
The structure of a Bayesian network (BN) encodes variable independence. Learning the structure of a ...
An analysis of Bayesian networks as classifiers is presented. This analysis results in an algorithm ...
Bayesian networks are directed acyclic graphs representing independence relationships among a set of...
Cada vez son más numerosas las investigaciones que trabajan con un amplio número de variables donde ...
The paper gives a few arguments in favour of use of chain graphs for description of probabilistic co...