This paper provides algorithms that use an information-theoretic analysis to learn Bayesian network structures from data. Based on our three-phase learning framework, we develop e...
Jie Cheng, Russell Greiner, Jonathan Kelly, David ...
The emerging interests in spatial pattern mining lead to the demand for a flexible spatial pattern mining language, on which easy to use and understand visual pattern language cou...
We analyze expression matrices to identify a priori interesting sets of genes, e.g., genes that are frequently co-regulated. Such matrices provide expression values for given biol...
It has been shown in prior work in management science, statistics and machine learning that using an ensemble of models often results in better performance than using a single ‘...
In the recent years, our ability of collecting information rapidly increases and huge databases that change over time in a high frequency have been developed. On the other hand, th...