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» Opportunistic Data Structures with Applications
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AMAI
2004
Springer
15 years 11 months ago
Using the Central Limit Theorem for Belief Network Learning
Learning the parameters (conditional and marginal probabilities) from a data set is a common method of building a belief network. Consider the situation where we have known graph s...
Ian Davidson, Minoo Aminian
ACSC
2003
IEEE
15 years 11 months ago
Context-Sensitive Mobile Database Summarisation
In mobile computing environments, as a result of the reduced capacity of local storage, it is commonly not feasible to replicate entire datasets on each mobile unit. In addition, ...
Darin Chan, John F. Roddick
PVLDB
2008
138views more  PVLDB 2008»
15 years 5 months ago
A skip-list approach for efficiently processing forecasting queries
Time series data is common in many settings including scientific and financial applications. In these applications, the amount of data is often very large. We seek to support pred...
Tingjian Ge, Stanley B. Zdonik
BMCBI
2010
117views more  BMCBI 2010»
15 years 6 months ago
Automatic, context-specific generation of Gene Ontology slims
Background: The use of ontologies to control vocabulary and structure annotation has added value to genomescale data, and contributed to the capture and re-use of knowledge across...
Melissa J. Davis, Muhammad Shoaib B. Sehgal, Mark ...
MICCAI
2010
Springer
15 years 4 months ago
Incorporating Priors on Expert Performance Parameters for Segmentation Validation and Label Fusion: A Maximum a Posteriori STAPL
Abstract. In order to evaluate the quality of segmentations of an image and assess intra- and inter-expert variability in segmentation performance, an Expectation Maximization (EM)...
Olivier Commowick, Simon K. Warfield