Add to ORKGI consider a binary classification problem with a feature vector of high dimensionality. Spam mail filters are a popular example hereof. A Bayesian approach requires us to estimate the probability of a feature vector given the class of the object. Due to the size of the feature vector this is an unfeasible t ask. A useful approach is to split the feature space into several (conditionally) independent subspaces. This results in a new problem, namely how to find the " best" subdivision. In this paper I consider a weighing approach that will perform (asymptotically) as good as the best subdivision and still has a manageable complexit
Incorporating subset selection into a classification method often carries a num-ber of advantages, e...
The author consider a binary classification problem with a feature vector of high dimensionality. Sp...
Incorporating subset selection into a classification method often carries a number of advantages, es...
I consider a binary classification problem with a feature vector of high dimensionality. Spam mail f...
I consider a binary classification problem with a feature vector of high dimensionality. Spam mail f...
The author consider a binary classification problem with a feature vector of high dimensionality. Sp...
The author consider a binary classification problem with a feature vector of high dimensionality. Sp...
I consider a binary classification problem with a feature vector of high dimensionality. Spam mail f...
In what follows, we introduce two Bayesian models for feature selection in high-dimensional data, sp...
In what follows, we introduce two Bayesian models for feature selection in high-dimensional data, sp...
The purpose of the present dissertation is to study model selection techniques which are specificall...
In what follows, we introduce two Bayesian models for feature selection in high-dimensional data, sp...
The author consider a binary classification problem with a feature vector of high dimensionality. Sp...
The author consider a binary classification problem with a feature vector of high dimensionality. Sp...
The author consider a binary classification problem with a feature vector of high dimensionality. Sp...
Incorporating subset selection into a classification method often carries a num-ber of advantages, e...
The author consider a binary classification problem with a feature vector of high dimensionality. Sp...
Incorporating subset selection into a classification method often carries a number of advantages, es...
I consider a binary classification problem with a feature vector of high dimensionality. Spam mail f...
I consider a binary classification problem with a feature vector of high dimensionality. Spam mail f...
The author consider a binary classification problem with a feature vector of high dimensionality. Sp...
The author consider a binary classification problem with a feature vector of high dimensionality. Sp...
I consider a binary classification problem with a feature vector of high dimensionality. Spam mail f...
In what follows, we introduce two Bayesian models for feature selection in high-dimensional data, sp...
In what follows, we introduce two Bayesian models for feature selection in high-dimensional data, sp...
The purpose of the present dissertation is to study model selection techniques which are specificall...
In what follows, we introduce two Bayesian models for feature selection in high-dimensional data, sp...
The author consider a binary classification problem with a feature vector of high dimensionality. Sp...
The author consider a binary classification problem with a feature vector of high dimensionality. Sp...
The author consider a binary classification problem with a feature vector of high dimensionality. Sp...
Incorporating subset selection into a classification method often carries a num-ber of advantages, e...
The author consider a binary classification problem with a feature vector of high dimensionality. Sp...
Incorporating subset selection into a classification method often carries a number of advantages, es...