In recent years there has been a flurry of works on learning probabilistic belief networks. Current state of the art methods have been shown to be successful for two learning scen...
Heuristic measures for estimating the quality of attributes mostly assume the independence of attributes so in domains with strong dependencies between attributes their performanc...
This paper is a comparative study of feature selection methods in statistical learning of text categorization. The focus is on aggressive dimensionality reduction. Five methods we...
Machine learning techniques are applicable to computer system optimization. We show that shared memory multiprocessors can successfully utilize machine learning algorithms for mem...
M. F. Sakr, Steven P. Levitan, Donald M. Chiarulli...
Abstract: We investigate the structure of model selection problems via the bias/variance decomposition. In particular, we characterize the essential structure of a model selection ...