The learning of probabilistic models with many hidden variables and nondecomposable dependencies is an important and challenging problem. In contrast to traditional approaches bas...
Although influence diagrams are powerful tools for representing and solving complex decisionmaking problems, their evaluation may require an enormous computational effort and this...
In this paper, the problem of time series prediction is studied. A Bayesian procedure based on Gaussian process models using a nonstationary covariance function is proposed. Exper...
In this paper, we present the way of fast and accurate estimation of software energy consumption in off-the-shelf processor using IPI(Inter-Prefetch Interval) energy model. In ou...
Jungsoo Kim, Kyungsu Kang, Heejun Shim, Woong Hwan...
This paper presents a novel opinion mining research problem, which is called Contrastive Opinion Modeling (COM). Given any query topic and a set of text collections from multiple ...