A max-2-connected Bayes network is one where there are at most 2 distinct directed paths between any two nodes. We show that even for this restricted topology, null-evidence belief updating is hard to approximate.

This paper investigates boosting naive Bayesian classiica-tion. It rst shows that boosting cannot improve the accuracy of the naive Bayesian classiier on average in a set of natural domains. By analyzing the reasons of boosting's failures, we propose to introduce tree structures into naive Bayesian classiication to improve the performance of boosting when working with naive Bayesian classiicati...

As one of the important applications of Web2.0 technology, blog attracts more and more users. Writing and browsing blog has become a popular hotspot of network culture, which promotes the development of blog search service. But, the current blog search engines are mostly only based on matching query keywords; lack the ability of automatically extracting users’ interests and recommendation. Real...

Search results visualization has emerged as an important research topic due to its application on search engine amelioration. From the perspective of machine learning, the text search results visualization task fits to the multi-label learning framework that a document is usually related to multiple category labels. In this paper, a Näıve Bayesian (NB) multi-label classification algorithm is pr...

text classification is an important research field in information retrieval and text mining. the main task in text classification is to assign text documents in predefined categories based on documents’ contents and labeled-training samples. since word detection is a difficult and time consuming task in persian language, bayesian text classifier is an appropriate approach to deal with different...

This paper proposes a multi-dimensional framework for classifying text documents. In this framework, the concept of multidimensional category model is introduced for representing classes. In contrast with traditional flat and hierarchical category models, the multi-dimensional category model classifies each text document in a collection using multiple predefined sets of categories, where each s...

LBR has demonstrated outstanding classification accuracy. However, it has high computational overheads when large numbers of instances are classified from a single training set. We compare LBR and the tree-augmented Bayesian classifier, and present a new heuristic LBR classifier that combines elements of the two. It requires less computation than LBR, but demonstrates similar prediction accuracy.

We employed a multilevel hierarchical Bayesian model in the task of exploiting relevant interactions among high cardinality attributes in a classification problem without overfitting. With this model, we calculate posterior class probabilities for a pattern W combining the observations of W in the training set with prior class probabilities that are obtained recursively from the observations of...