Abstract. We propose and analyze a new vantage point for the learning of mixtures of Gaussians: namely, the PAC-style model of learning probability distributions introduced by Kear...
To deal with data uncertainty, existing probabilistic database systems augment tuples with attribute-level or tuple-level probability values, which are loaded into the database al...
Ravi Jampani, Fei Xu, Mingxi Wu, Luis Leopoldo Per...
Abstract: Our main contribution is to propose a novel model selection methodology, expectation minimization of information criterion (EMIC). EMIC makes a significant impact on the...
on Uncertain Data (Extended Abstract) Ming Hua Jian Pei Wenjie Zhang Xuemin Lin Simon Fraser University, Canada The University of New South Wales & NICTA {mhua, jpei}@cs.sfu.c...
Abstract. We present an approach to support incremental navigation of structured information, where the structure is introduced by the data model and schema (if present) of a data ...