Processing Obtained Email Data By Using Naïve Bayes Learning Algorithm

Email Worm Detection Using Naïve Bayes and Support Vector Machine

Learning Semi Naïve Bayes Structures by Estimation of Distribution Algorithms

Recent work in supervised learning has shown that a surprisingly simple Bayesian classifier called naı̈ve Bayes is competitive with state of the art classifiers. This simple approach stands from assumptions of conditional independence among features given the class. Improvements in accuracy of naı̈ve Bayes has been demonstrated by a number of approaches, collectively named semi naı̈ve Bayes classi...

Image Classification Using Naïve Bayes Classifier

An image classification scheme using Naïve Bayes Classifier is proposed in this paper. The proposed Naive Bayes Classifier-based image classifier can be considered as the maximum a posteriori decision rule. The Naïve Bayes Classifier can produce very accurate classification results with a minimum training time when compared to conventional supervised or unsupervised learning algorithms. Compreh...

Collaborative Filtering Using Interval Estimation Naïve Bayes

Personalized recommender systems can be classified into three main categories: content-based, mostly used to make suggestions depending on the text of the web documents, collaborative filtering, that use ratings from many users to suggest a document or an action to a given user and hybrid solutions. In the collaborative filtering task we can find algorithms such as the naı̈ve Bayes classifier or...

Naı̈ve-Bayes vs. Rule-Learning in Classification of Email

Recent growth in the use of email for communication and the corresponding growth in the volume of email received has made automatic processing of email desirable. Two learning methods, naı̈ve bayesian learning with bag-valued features and the RIPPER rule-learning algorithm have shown promise in other text categorization tasks. I present three experiments in automatic mail foldering and spam filt...