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» Distributed data mining in grid computing environments
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SDM
2009
SIAM
117views Data Mining» more  SDM 2009»
14 years 5 months ago
Spatially Cost-Sensitive Active Learning.
In active learning, one attempts to maximize classifier performance for a given number of labeled training points by allowing the active learning algorithm to choose which points...
Alexander Liu, Goo Jun, Joydeep Ghosh
ADBIS
1999
Springer
104views Database» more  ADBIS 1999»
14 years 6 days ago
Arbiter Meta-Learning with Dynamic Selection of Classifiers and Its Experimental Investigation
In data mining, the selection of an appropriate classifier to estimate the value of an unknown attribute for a new instance has an essential impact to the quality of the classifica...
Alexey Tsymbal, Seppo Puuronen, Vagan Y. Terziyan
NAR
2006
177views more  NAR 2006»
13 years 7 months ago
PUMA2 - grid-based high-throughput analysis of genomes and metabolic pathways
The PUMA2 system (available at http://compbio.mcs. 10 anl.gov/puma2) is an interactive, integrated bioinformatics environment for high-throughput genetic sequence analysis and met...
Natalia Maltsev, Elizabeth M. Glass, Dinanath Sula...
ICDCS
1995
IEEE
13 years 11 months ago
Parallel Processing on Networks of Workstations: A Fault-Tolerant, High Performance Approach
One of the mostsoughtaftersoftware innovation of thisdecade is the construction of systems using off-the-shelf workstations that actually deliver, and even surpass, the power and ...
Partha Dasgupta, Zvi M. Kedem, Michael O. Rabin
KDD
2009
ACM
191views Data Mining» more  KDD 2009»
14 years 8 months ago
Scalable pseudo-likelihood estimation in hybrid random fields
Learning probabilistic graphical models from high-dimensional datasets is a computationally challenging task. In many interesting applications, the domain dimensionality is such a...
Antonino Freno, Edmondo Trentin, Marco Gori