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ALGORITHMICA
2006
74views more  ALGORITHMICA 2006»
15 years 6 months ago
Parallelizing Feature Selection
Classification is a key problem in machine learning/data mining. Algorithms for classification have the ability to predict the class of a new instance after having been trained on...
Jerffeson Teixeira de Souza, Stan Matwin, Nathalie...
NAR
2007
135views more  NAR 2007»
15 years 6 months ago
g: Profiler - a web-based toolset for functional profiling of gene lists from large-scale experiments
g:Profiler (http://biit.cs.ut.ee/gprofiler/) is a public web server for characterising and manipulating gene lists resulting from mining high-throughput genomic data. g:Profiler h...
Jüri Reimand, Meelis Kull, Hedi Peterson, Jaa...
TNN
2010
148views Management» more  TNN 2010»
15 years 1 months ago
A fast algorithm for robust mixtures in the presence of measurement errors
Abstract--In experimental and observational sciences, detecting atypical, peculiar data from large sets of measurements has the potential of highlighting candidates of interesting ...
Jianyong Sun, Ata Kabán
CORR
2000
Springer
120views Education» more  CORR 2000»
15 years 6 months ago
Scaling Up Inductive Logic Programming by Learning from Interpretations
When comparing inductive logic programming (ILP) and attribute-value learning techniques, there is a trade-off between expressive power and efficiency. Inductive logic programming ...
Hendrik Blockeel, Luc De Raedt, Nico Jacobs, Bart ...
INFFUS
2008
97views more  INFFUS 2008»
15 years 6 months ago
Using classifier ensembles to label spatially disjoint data
act 11 We describe an ensemble approach to learning from arbitrarily partitioned data. The partitioning comes from the distributed process12 ing requirements of a large scale simul...
Larry Shoemaker, Robert E. Banfield, Lawrence O. H...