We propose an agent for exploring and categorizing documents on the World Wide Web based on a user pro le. The heart of the agent is an automatic categorization of a set of docume...
Eui-Hong Han, Daniel Boley, Maria L. Gini, Robert ...
The latent topic model plays an important role in the unsupervised learning from a corpus, which provides a probabilistic interpretation of the corpus in terms of the latent topic...
Abstract. In this paper we propose the clustering of top-ranking sentences (TRS) for effective information access. Top-ranking sentences are selected by a query-biased sentence ex...
Abstract. We present a clustering method for continuous data. It defines local clusters into the (primary) data space but derives its similarity measure from the posterior distribu...
Text classification is the process of classifying documents into predefined categories based on their content. Existing supervised learning algorithms to automatically classify te...