It is well known that in unidentifiable models, the Bayes estimation has the advantage of generalization performance to the maximum likelihood estimation. However, accurate approximation of the posterior distribution requires huge computational costs. In this paper, we consider an empirical Bayes approach where a part of the parameters are regarded as hyperparameters, which we call a subspace B...

A novel relational learning approach that tightly integrates the naı̈ve Bayes learning scheme with the inductive logic programming rule-learner FOIL is presented. In contrast to previous combinations that have employed naı̈ve Bayes only for post-processing the rule sets, the presented approach employs the naı̈ve Bayes criterion to guide its search directly. The proposed technique is implemented in...

In this paper investigation of the performance criterion of a machine learning tool, Naive Bayes Classifier with a new weighted approach in classifying breast cancer is done . Naive Bayes is one of the most effective classification algorithms. In many decision making system, ranking performance is an interesting and desirable concept than just classification. So to extend traditional Naive Baye...

user-generated medical messages on internet contain extensive information related to adverse drug reactions (adrs) and are known as valuable resources for post-marketing drug surveillance. the aim of this study was to find an effective method to identify messages related to adrs automatically from online user reviews.we conducted experiments on online user reviews using different feature set an...

Summary Loss-based clustering methods, such as k-means and its variants, are standard tools for finding groups in data. However, the lack of quantification uncertainty estimated clusters is a disadvantage. Model-based based on mixture models provides an alternative approach, but methods face computational problems highly sensitive to choice kernel. In this article we propose generalized Bayes f...