This paper presents a novel time varying dynamic Bayesian network (TVDBN) model for the
analysis of nonstationary sequences which are of interest in many fields. The changing …

Large scale library digitization projects such as the Open Content Alliance are producing
vast quantities of text, but little has been done to organize this data. Subject headings …

(57) ABSTRACT A classification method includes constructing queries from category
descriptors representing categories of a taxonomy of hierarchically organized categories …

A topic taxonomy is an effective representation that describes salient features of virtual
groups or online communities. A topic taxonomy consists of topic nodes. Each internal node …

The traditional machine learning approaches for text classification often require labelled
data for learning classifiers. However, when applied to large-scale classification involving …

This article analyzes the data quality issues that emerge when training a shrinkage-based
classifier with noisy data. A statistical text analysis technique based on a shrinkage-based …

The paper represents a first attempt to formalize the get-specific document classification
algorithm and to fully automate it through reasoning in a propositional concept language …

Advances in computing and multimedia technologies allow many accounting documents to
be digitized within little cost for effective storage and access. Moreover, the amount of …

The problem that we address in this paper is that of learning hierarchical object categories.
Indeed, Digital media technology generates huge amount of non-textual information …

The problem that we elaborate in this work is develo** and comparing statistical models
for learning hierarchical image categories from a structural point of view. Previously different …