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Recent work in supervised learning has shown that a surprisingly simple Bayesian classifier with strong assumptions of independence among features, called naive Bayes, is competitive with state-of-the-art classifiers such as C4.5. This fact raises the question of whether a classifier with less restrictive assumptions can perform even better. In this paper(More)
We describe a Bayesian approach for learning Bayesian networks from a combination of prior knowledge and statistical data. First and foremost, we develop a methodology for assessing informative priors needed for learning. Our approach is derived from a set of assumptions made previously as well as the assumption of likelihood equivalence, which says that(More)
We describe scoring metrics for learning Bayesian networks from a combination of user knowledge and statistical data. Previ­ ous work has concentrated on metrics for do­ mains containing only discrete variables, un­ der the assumption that data represents a multinomial sample. In this paper, we ex­ tend this work, developing scoring metrics for domains(More)
We consider the problem of performing Nearest-neighbor queries efficiently over large high-dimensional databases. To avoid a full database scan, we target constructing a multidimensional index structure. It is well-accepted that traditional database indexing algorithms fail for high-dimensional data (say d > 10 or 20 depending on the scheme). Some arguments(More)
This paper explores the role of Directed Acyclic Graphs (DAGs) as a representation of condition­ al independence relationships. We show that DAGs offer polynomially sound and complete inference mechanisms for inferring conditional independence relationships from a given causal set of such relation­ ships. As a consequence, d-separation, a graphical(More)