Bayesian text classifiers face a common issue which is referred to as data sparsity problem, especially when the size of training data is very small. The frequently used Laplacian...
Recent work has exploited boundedness of data in the unsupervised learning of new types of generative model. For nonnegative data it was recently shown that the maximum-entropy ge...
This paper describes a new method for extracting open compounds (uninterrupted sequences of words) from text corpora of languages, such as Thai, Japanese and Korea that exhibit un...
This paper presents a Bayesian approach to learning the connectivity structure of a group of neurons from data on configuration frequencies. A major objective of the research is t...
Background: There are several isolated tools for partial analysis of microarray expression data. To provide an integrative, easy-to-use and automated toolkit for the analysis of A...
Gabriela G. Loots, Patrick S. G. Chain, Shalini Ma...