The problem of combining the ranked preferences of many experts is an old and surprisingly deep problem that has gained renewed importance in many machine learning, data mining, a...
In this paper we present the Dynamic Grow-Shrink Inference-based Markov network learning algorithm (abbreviated DGSIMN), which improves on GSIMN, the state-ofthe-art algorithm for...
The main goal of web pages ranking is to find the interrelated pages. In this paper, we introduce an algorithm called FPR-DLA. In the proposed method learning automata is assigned...
We present recent results from the LCDM3 collaboration between UIUC Astronomy and NCSA to deploy supercomputing cluster resources and machine learning algorithms for the mining of ...
Nicholas M. Ball, Robert J. Brunner, Adam D. Myers
Much work on skewed, stochastic, high dimensional, and biased datasets usually implicitly solve each problem separately. Recently however, we have been approached by Texas Commiss...
Kun Zhang, Wei Fan, Xiaojing Yuan, Ian Davidson, X...