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Averaged one-dependence estimators (AODE) is a type of supervised learning algorithm that relaxes the conditional independence assumption that governs standard naïve Bayes learning algorithms. AODE has demonstrated reasonable improvement in terms of classification performance when compared with a naïve Bayes learner. However, AODE does not consider the relationships between the super-parent attribute...
Naïve Bayes learners are widely used, efficient, and effective supervised learning methods for labeled datasets in noisy environments. It has been shown that naïve Bayes learners produce reasonable performance compared with other machine learning algorithms. However, the conditional independence assumption of naïve Bayes learning imposes restrictions on the handling of real-world data. To relax the...
Micro-blogging is widely used nowadays. Most of the users normally write down their daily experience, feeling or emotion on their wall. This includes uploading photos of food they have taken. Some friends of this user might be interested in trying this food of which the photo has been uploaded by this user. However, it could be difficult for the friends to search that particular food on the Web as...
In this paper, we propose NewsFeedAndroid, a novel system that interconnects a social networking service and online newspaper sites in order to extracts news articles from the online news sites and to perform feeding of news articles to social network service (SNS) users. In NewsFeedAndroid, news information agents extract news article information from the news and portal sites using Minimum Description...
This study proposes a method that classifies Chinese social network positive-negative comments (Weibo) using naive Bayes algorithm trained from English social network (Twitter) corpus. We train our text classifier using Twitter corpus (in English language), and use this classifier to classify Chinese text. In the previous research, Chinese sentences are processed using Chinese word segmentation algorithms...
Naive Bayes (NB) learning is more popular, faster and effective supervised learning method to handle the labeled datasets especially in which have some noises, NB learning also has well performance. However, the conditional independent assumption of NB learning imposes some restriction on the property of handling data of real world. Some researchers proposed lots of methods to relax NB assumption,...
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