This paper reports our investigation on the problem of belief update in Bayesian networks (BN) using uncertain evidence. We focus on two types of uncertain evidences, virtual evid...
Simulation built on assumption and approximation has been traditionally utilized to make predictions prior to construction. Although there are many benefits of simulation such as ...
Tae Hwan Chung, Yasser Mohamed, Simaan M. AbouRizk
The expectation maximization (EM) algorithm is a popular algorithm for parameter estimation in models with hidden variables. However, the algorithm has several non-trivial limitat...
Learning Classifier Systems differ from many other classification techniques, in that new rules are constantly discovered and evaluated. This feature of LCS gives rise to an im...
The task of learning models for many real-world problems requires incorporating domain knowledge into learning algorithms, to enable accurate learning from a realistic volume of t...
Radu Stefan Niculescu, Tom M. Mitchell, R. Bharat ...