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» Methods for finding frequent items in data streams
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SDM
2012
SIAM
216views Data Mining» more  SDM 2012»
11 years 9 months ago
Feature Selection "Tomography" - Illustrating that Optimal Feature Filtering is Hopelessly Ungeneralizable
:  Feature Selection “Tomography” - Illustrating that Optimal Feature Filtering is Hopelessly Ungeneralizable George Forman HP Laboratories HPL-2010-19R1 Feature selection; ...
George Forman
BIODATAMINING
2008
96views more  BIODATAMINING 2008»
13 years 7 months ago
Fast approximate hierarchical clustering using similarity heuristics
Background: Agglomerative hierarchical clustering (AHC) is a common unsupervised data analysis technique used in several biological applications. Standard AHC methods require that...
Meelis Kull, Jaak Vilo
CPHYSICS
2006
127views more  CPHYSICS 2006»
13 years 7 months ago
GenAnneal: Genetically modified Simulated Annealing
A modification of the standard Simulated Annealing (SA) algorithm is presented for finding the global minimum of a continuous multidimensional, multimodal function. We report resu...
Ioannis G. Tsoulos, Isaac E. Lagaris
CVPR
2008
IEEE
14 years 9 months ago
Mining compositional features for boosting
The selection of weak classifiers is critical to the success of boosting techniques. Poor weak classifiers do not perform better than random guess, thus cannot help decrease the t...
Junsong Yuan, Jiebo Luo, Ying Wu
JCB
2008
159views more  JCB 2008»
13 years 6 months ago
BayesMD: Flexible Biological Modeling for Motif Discovery
We present BayesMD, a Bayesian Motif Discovery model with several new features. Three different types of biological a priori knowledge are built into the framework in a modular fa...
Man-Hung Eric Tang, Anders Krogh, Ole Winther