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KDD
2012
ACM
187views Data Mining» more  KDD 2012»
7 years 10 months ago
Sampling minimal frequent boolean (DNF) patterns
We tackle the challenging problem of mining the simplest Boolean patterns from categorical datasets. Instead of complete enumeration, which is typically infeasible for this class ...
Geng Li, Mohammed J. Zaki
COMSNETS
2012
183views more  COMSNETS 2012»
8 years 3 months ago
Effects of network trace sampling methods on privacy and utility metrics
—Researchers choosing to share wireless-network traces with colleagues must first anonymize sensitive information, trading off the removal of information in the interest of iden...
Phil Fazio, Keren Tan, David Kotz
ICASSP
2011
IEEE
8 years 11 months ago
Langevin and hessian with fisher approximation stochastic sampling for parameter estimation of structured covariance
We have studied two efficient sampling methods, Langevin and Hessian adapted Metropolis Hastings (MH), applied to a parameter estimation problem of the mathematical model (Lorent...
Cornelia Vacar, Jean-François Giovannelli, ...
BMCBI
2007
148views more  BMCBI 2007»
9 years 8 months ago
Blast sampling for structural and functional analyses
Background: The post-genomic era is characterised by a torrent of biological information flooding the public databases. As a direct consequence, similarity searches starting with ...
Anne Friedrich, Raymond Ripp, Nicolas Garnier, Emm...
IFIP12
2008
9 years 9 months ago
A Study with Class Imbalance and Random Sampling for a Decision Tree Learning System
Sampling methods are a direct approach to tackle the problem of class imbalance. These methods sample a data set in order to alter the class distributions. Usually these methods ar...
Ronaldo C. Prati, Gustavo E. A. P. A. Batista, Mar...
ESEM
2007
ACM
9 years 12 months ago
The Effects of Over and Under Sampling on Fault-prone Module Detection
The goal of this paper is to improve the prediction performance of fault-prone module prediction models (fault-proneness models) by employing over/under sampling methods, which ar...
Yasutaka Kamei, Akito Monden, Shinsuke Matsumoto, ...
RT
2001
Springer
10 years 15 days ago
Interleaved Sampling
The known sampling methods can roughly be grouped into regular and irregular sampling. While regular sampling can be realized efficiently in graphics hardware, it is prone to inte...
Alexander Keller, Wolfgang Heidrich
SIGGRAPH
2010
ACM
10 years 17 days ago
Multi-class blue noise sampling
Sampling is a core process for a variety of graphics applications. Among existing sampling methods, blue noise sampling remains popular thanks to its spatial uniformity and absenc...
Li-Yi Wei
ICDE
2007
IEEE
155views Database» more  ICDE 2007»
10 years 2 months ago
Query-Aware Sampling for Data Streams
Data Stream Management Systems are useful when large volumes of data need to be processed in real time. Examples include monitoring network traffic, monitoring financial transacti...
Theodore Johnson, S. Muthukrishnan, Vladislav Shka...
GLOBECOM
2007
IEEE
10 years 2 months ago
Control Estimation Error of Sampling Method for Passive Measurement
1 — Sampling is increasingly utilized by passive measurement systems to save the resources consumption. However, the widely adopted static linear sampling selects packets with th...
Chengchen Hu, Sheng Wang, Jia Tian, Bin Liu
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