For an augmented Bayesian network classifier we propose a method of scoring a set of feature nodes for the separation strength, wherein we have combined a weighting technique and growing dimension asymptotics in a single framework. We show that the distribution of the weighted classifier is asymptotically Gaussian and establish the weight-function which is optimal in a sense of minimum misclassification probability
This work proposes and discusses an approach for inducing Bayesian classifiers aimed at balancing th...
The most important feature of a classifier is its generalisation capability. It depends on the corre...
An analysis of Bayesian networks as classifiers is presented. This analysis results in an algorithm ...
Incorporating subset selection into a classification method often carries a number of advantages, es...
Incorporating subset selection into a classification method often carries a num-ber of advantages, e...
We introduce a Bayesian network classifier less restrictive than Naive Bayes (NB) and Tree Augmented...
The use of Bayesian networks for classification problems has received significant recent attention. ...
We introduce the family of multi-dimensional Bayesian network classifiers. These clas-sifiers includ...
Abstract. We describe the family of multi-dimensional Bayesian network clas-siers which include one ...
Multi-dimensional classification aims at finding a function that assigns a vector of class values to...
AbstractMulti-dimensional classification aims at finding a function that assigns a vector of class v...
AbstractThe margin criterion for parameter learning in graphical models gained significant impact ov...
We consider the problem of learning Bayesian network classifiers that maximize the margin over a set...
Multi-dimensional Bayesian network classifiers are Bayesian networks of restricted topological struc...
AbstractThe use of Bayesian Networks (BNs) as classifiers in different fields of application has rec...
This work proposes and discusses an approach for inducing Bayesian classifiers aimed at balancing th...
The most important feature of a classifier is its generalisation capability. It depends on the corre...
An analysis of Bayesian networks as classifiers is presented. This analysis results in an algorithm ...
Incorporating subset selection into a classification method often carries a number of advantages, es...
Incorporating subset selection into a classification method often carries a num-ber of advantages, e...
We introduce a Bayesian network classifier less restrictive than Naive Bayes (NB) and Tree Augmented...
The use of Bayesian networks for classification problems has received significant recent attention. ...
We introduce the family of multi-dimensional Bayesian network classifiers. These clas-sifiers includ...
Abstract. We describe the family of multi-dimensional Bayesian network clas-siers which include one ...
Multi-dimensional classification aims at finding a function that assigns a vector of class values to...
AbstractMulti-dimensional classification aims at finding a function that assigns a vector of class v...
AbstractThe margin criterion for parameter learning in graphical models gained significant impact ov...
We consider the problem of learning Bayesian network classifiers that maximize the margin over a set...
Multi-dimensional Bayesian network classifiers are Bayesian networks of restricted topological struc...
AbstractThe use of Bayesian Networks (BNs) as classifiers in different fields of application has rec...
This work proposes and discusses an approach for inducing Bayesian classifiers aimed at balancing th...
The most important feature of a classifier is its generalisation capability. It depends on the corre...
An analysis of Bayesian networks as classifiers is presented. This analysis results in an algorithm ...