Add to ORKGAbstract. This paper presents the method of significantly improving conventional Bayesian statistical text classifier by incorporating ac-celerated EM (Expectation Maximization) algorithm. EM algorithm experiences a slow convergence and performance degrade in its iterative process, especially when real textual documents do not follow EM’s assumptions. We propose a new accelerated EM algorithm that is simple yet has a fast convergence speed and allow to estimate a more accurate classification model on Bayesian text classifier
Document classification is a growing interest in the research of text mining. Classification can be ...
Ce document présente des techniques de base entrant dans les techniques probabilistes d'indexation d...
We introduce a Bayesian model, BayesANIL, that is capable of estimating uncertainties as-sociated wi...
This paper shows that the accuracy of learned text classifiers can be improved by augmenting a small...
Aiming at the phenomenon that in text classification the calculation of prior probability is time-co...
Multinomial Naive Bayes with Expectation Maximization (MNB-EM) is a standard semi-supervised learnin...
Naive Bayes is often used as a baseline in text classification because it is fast and easy to implem...
Abstract. Smoothing is applied in Bayes classifier when the maximum likelihood (ML) estimate can’t s...
Though naïve Bayes text classifiers are widely used because of its simplicity and effectiveness, the...
AbstractNaïve Bayes classifiers which are widely used for text classification in machine learning ar...
With the development of Internet and the emergence of a large number of text resources, the automati...
The enormous amount of information stored in unstructured texts cannot simply be used for further pr...
We propose a human-cognition inspired classification model based on Naïve Bayes. Our previous study ...
The most important feature of a classifier is its generalisation capability. It depends on the corre...
Combining a naive Bayes classifier with the EM algorithm is one of the promising ap-proaches for mak...
Document classification is a growing interest in the research of text mining. Classification can be ...
Ce document présente des techniques de base entrant dans les techniques probabilistes d'indexation d...
We introduce a Bayesian model, BayesANIL, that is capable of estimating uncertainties as-sociated wi...
This paper shows that the accuracy of learned text classifiers can be improved by augmenting a small...
Aiming at the phenomenon that in text classification the calculation of prior probability is time-co...
Multinomial Naive Bayes with Expectation Maximization (MNB-EM) is a standard semi-supervised learnin...
Naive Bayes is often used as a baseline in text classification because it is fast and easy to implem...
Abstract. Smoothing is applied in Bayes classifier when the maximum likelihood (ML) estimate can’t s...
Though naïve Bayes text classifiers are widely used because of its simplicity and effectiveness, the...
AbstractNaïve Bayes classifiers which are widely used for text classification in machine learning ar...
With the development of Internet and the emergence of a large number of text resources, the automati...
The enormous amount of information stored in unstructured texts cannot simply be used for further pr...
We propose a human-cognition inspired classification model based on Naïve Bayes. Our previous study ...
The most important feature of a classifier is its generalisation capability. It depends on the corre...
Combining a naive Bayes classifier with the EM algorithm is one of the promising ap-proaches for mak...
Document classification is a growing interest in the research of text mining. Classification can be ...
Ce document présente des techniques de base entrant dans les techniques probabilistes d'indexation d...
We introduce a Bayesian model, BayesANIL, that is capable of estimating uncertainties as-sociated wi...