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» Comparing Massive High-Dimensional Data Sets
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SODA
2008
ACM
126views Algorithms» more  SODA 2008»
13 years 8 months ago
On distributing symmetric streaming computations
A common approach for dealing with large data sets is to stream over the input in one pass, and perform computations using sublinear resources. For truly massive data sets, howeve...
Jon Feldman, S. Muthukrishnan, Anastasios Sidiropo...
ICDM
2010
IEEE
264views Data Mining» more  ICDM 2010»
13 years 5 months ago
Block-GP: Scalable Gaussian Process Regression for Multimodal Data
Regression problems on massive data sets are ubiquitous in many application domains including the Internet, earth and space sciences, and finances. In many cases, regression algori...
Kamalika Das, Ashok N. Srivastava
BMCBI
2010
147views more  BMCBI 2010»
13 years 7 months ago
baySeq: Empirical Bayesian methods for identifying differential expression in sequence count data
Background: High throughput sequencing has become an important technology for studying expression levels in many types of genomic, and particularly transcriptomic, data. One key w...
Thomas J. Hardcastle, Krystyna A. Kelly
KDD
2004
ACM
131views Data Mining» more  KDD 2004»
14 years 7 months ago
Fast nonlinear regression via eigenimages applied to galactic morphology
Astronomy increasingly faces the issue of massive datasets. For instance, the Sloan Digital Sky Survey (SDSS) has so far generated tens of millions of images of distant galaxies, ...
Brigham Anderson, Andrew W. Moore, Andrew Connolly...
ANOR
2010
130views more  ANOR 2010»
13 years 4 months ago
Greedy scheduling with custom-made objectives
We present a methodology to automatically generate an online job scheduling method for a custom-made objective and real workloads. The scheduling problem comprises independent para...
Carsten Franke, Joachim Lepping, Uwe Schwiegelshoh...