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» Accelerating Large Data Analysis by Exploiting Regularities
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ICPR
2008
IEEE
14 years 7 months ago
Exploiting qualitative domain knowledge for learning Bayesian network parameters with incomplete data
When a large amount of data are missing, or when multiple hidden nodes exist, learning parameters in Bayesian networks (BNs) becomes extremely difficult. This paper presents a lea...
Qiang Ji, Wenhui Liao
PODS
2012
ACM
240views Database» more  PODS 2012»
11 years 8 months ago
Approximate computation and implicit regularization for very large-scale data analysis
Database theory and database practice are typically the domain of computer scientists who adopt what may be termed an algorithmic perspective on their data. This perspective is ve...
Michael W. Mahoney
SDM
2007
SIAM
137views Data Mining» more  SDM 2007»
13 years 7 months ago
Semi-supervised Feature Selection via Spectral Analysis
Feature selection is an important task in effective data mining. A new challenge to feature selection is the so-called “small labeled-sample problem” in which labeled data is...
Zheng Zhao, Huan Liu
VISUALIZATION
2005
IEEE
13 years 12 months ago
Query-Driven Visualization of Large Data Sets
We present a practical and general-purpose approach to large and complex visual data analysis where visualization processing, rendering and subsequent human interpretation is cons...
Kurt Stockinger, John Shalf, Kesheng Wu, E. Wes Be...
CF
2009
ACM
13 years 11 months ago
Data parallel acceleration of decision support queries using Cell/BE and GPUs
Decision Support System (DSS) workloads are known to be one of the most time-consuming database workloads that processes large data sets. Traditionally, DSS queries have been acce...
Pedro Trancoso, Despo Othonos, Artemakis Artemiou