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UAI
1996
15 years 8 months ago
Efficient Approximations for the Marginal Likelihood of Incomplete Data Given a Bayesian Network
We discuss Bayesian methods for learning Bayesian networks when data sets are incomplete. In particular, we examine asymptotic approximations for the marginal likelihood of incomp...
David Maxwell Chickering, David Heckerman
ICDE
2011
IEEE
338views Database» more  ICDE 2011»
14 years 10 months ago
Outlier detection on uncertain data: Objects, instances, and inferences
—This paper studies the problem of outlier detection on uncertain data. We start with a comprehensive model considering both uncertain objects and their instances. An uncertain o...
Bin Jiang, Jian Pei
ACSW
2007
15 years 8 months ago
Controlling Inference: Avoiding P-level Reduction during Analysis
This paper presents a concept hierarchy-based approach to privacy preserving data collection for data mining called the P-level model. The P-level model allows data providers to d...
Adepele Williams, Ken Barker
KDD
2007
ACM
142views Data Mining» more  KDD 2007»
16 years 7 months ago
Towards Privacy-Preserving Model Selection
Abstract. Model selection is an important problem in statistics, machine learning, and data mining. In this paper, we investigate the problem of enabling multiple parties to perfor...
Zhiqiang Yang, Sheng Zhong, Rebecca N. Wright
BMCBI
2005
122views more  BMCBI 2005»
15 years 6 months ago
FACT - a framework for the functional interpretation of high-throughput experiments
Background: Interpreting the results of high-throughput experiments, such as those obtained from DNA-microarrays, is an often time-consuming task due to the high number of data-po...
Felix Kokocinski, Nicolas Delhomme, Gunnar Wrobel,...