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» A distributed machine learning framework
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104
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CCGRID
2009
IEEE
15 years 7 months ago
Performance Issues in Parallelizing Data-Intensive Applications on a Multi-core Cluster
The deluge of available data for analysis demands the need to scale the performance of data mining implementations. With the current architectural trends, one of the major challen...
Vignesh T. Ravi, Gagan Agrawal
96
Voted
NIPS
2000
15 years 2 months ago
Vicinal Risk Minimization
The Vicinal Risk Minimization principle establishes a bridge between generative models and methods derived from the Structural Risk Minimization Principle such as Support Vector M...
Olivier Chapelle, Jason Weston, Léon Bottou...
126
Voted
SIGIR
2012
ACM
13 years 3 months ago
Robust ranking models via risk-sensitive optimization
Many techniques for improving search result quality have been proposed. Typically, these techniques increase average effectiveness by devising advanced ranking features and/or by...
Lidan Wang, Paul N. Bennett, Kevyn Collins-Thompso...
101
Voted
AIMSA
2008
Springer
15 years 7 months ago
Incorporating Learning in Grid-Based Randomized SAT Solving
Abstract. Computational Grids provide a widely distributed computing environment suitable for randomized SAT solving. This paper develops techniques for incorporating learning, kno...
Antti Eero Johannes Hyvärinen, Tommi A. Juntt...
129
Voted
JSS
2002
198views more  JSS 2002»
15 years 14 days ago
Automated discovery of concise predictive rules for intrusion detection
This paper details an essential component of a multi-agent distributed knowledge network system for intrusion detection. We describe a distributed intrusion detection architecture...
Guy G. Helmer, Johnny S. Wong, Vasant Honavar, Les...