Add to ORKGIn a classification problem, we would like to assign a model to the observed data using its features. If the number of these features is large, considering dependencies between them to come up with an appropriate model becomes a challenge. In this paper an efficient method is introduced for selecting a model that fits the observed data sequence with a large number of features. This method is constructed by a modification of an earlier studied algorithm. It is shown that the model selected by this method has the Maximum Likelihood probability and can capture the dependencies between the features of the data sequence. We prove that through this method, the algebraic workload required for calculating the Maximum Likelihood model is reduced
The amount of information in the form of features and variables avail-able to machine learning algor...
\u3cp\u3eFeature selection and inference through modeling are combined into one method based on a ne...
The paper addresses the problem of making dependency-aware feature selection feasible in pattern rec...
In a classification problem, we would like to assign a model to the observed data using its features...
In the present paper, we study the problem of model selection for classification of high-dimensional...
In the present paper, we study the problem of model selection for classification of high-dimensional...
We here introduce a novel classification approach adopted from the nonlinear model identification fr...
We here introduce a novel classification approach adopted from the nonlinear model identification fr...
We here introduce a novel classification approach adopted from the nonlinear model identification fr...
We here introduce a novel classification approach adopted from the nonlinear model identification fr...
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...
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...
Feature selection and inference through modeling are combined into one method based on a network tha...
The amount of information in the form of features and variables avail-able to machine learning algor...
\u3cp\u3eFeature selection and inference through modeling are combined into one method based on a ne...
The paper addresses the problem of making dependency-aware feature selection feasible in pattern rec...
In a classification problem, we would like to assign a model to the observed data using its features...
In the present paper, we study the problem of model selection for classification of high-dimensional...
In the present paper, we study the problem of model selection for classification of high-dimensional...
We here introduce a novel classification approach adopted from the nonlinear model identification fr...
We here introduce a novel classification approach adopted from the nonlinear model identification fr...
We here introduce a novel classification approach adopted from the nonlinear model identification fr...
We here introduce a novel classification approach adopted from the nonlinear model identification fr...
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...
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...
Feature selection and inference through modeling are combined into one method based on a network tha...
The amount of information in the form of features and variables avail-able to machine learning algor...
\u3cp\u3eFeature selection and inference through modeling are combined into one method based on a ne...
The paper addresses the problem of making dependency-aware feature selection feasible in pattern rec...