This paper presents a general and efficient framework for probabilistic inference and learning from arbitrary uncertain information. It exploits the calculation properties of fini...
In this paper, we discuss round robin classification (aka pairwise classification), a technique for handling multi-class problems with binary classifiers by learning one classifie...
We define an optimal class association rule set to be the minimum rule set with the same predictive power of the complete class association rule set. Using this rule set instead o...
Users need more sophisticated tools to handle the growing number of image-based documents available in databases. In this paper, we present a system devoted to the editing and bro...
The Gene Expression Omnibus (GEO) project was initiated in response to the growing demand for a public repository for high-throughput gene expression data. GEO provides a flexible...