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» Parallel Induction Algorithms for Large Samples
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CORR
2010
Springer
153views Education» more  CORR 2010»
13 years 7 months ago
GraphLab: A New Framework for Parallel Machine Learning
Designing and implementing efficient, provably correct parallel machine learning (ML) algorithms is challenging. Existing high-level parallel abstractions like MapReduce are insuf...
Yucheng Low, Joseph Gonzalez, Aapo Kyrola, Danny B...
FSS
2010
147views more  FSS 2010»
13 years 6 months ago
A divide and conquer method for learning large Fuzzy Cognitive Maps
Fuzzy Cognitive Maps (FCMs) are a convenient tool for modeling and simulating dynamic systems. FCMs were applied in a large number of diverse areas and have already gained momentu...
Wojciech Stach, Lukasz A. Kurgan, Witold Pedrycz
BMCBI
2008
133views more  BMCBI 2008»
13 years 7 months ago
A Web-based and Grid-enabled dChip version for the analysis of large sets of gene expression data
Background: Microarray techniques are one of the main methods used to investigate thousands of gene expression profiles for enlightening complex biological processes responsible f...
Luca Corradi, Marco Fato, Ivan Porro, Silvia Scagl...
ICDCS
2005
IEEE
14 years 1 months ago
Characterizing and Predicting TCP Throughput on the Wide Area Network
DualPats exploits the strong correlation between TCP throughput and flow size, and the statistical stability of Internet path characteristics to accurately predict the TCP throug...
Dong Lu, Yi Qiao, Peter A. Dinda, Fabián E....
PC
2007
343views Management» more  PC 2007»
13 years 7 months ago
Runtime scheduling of dynamic parallelism on accelerator-based multi-core systems
We explore runtime mechanisms and policies for scheduling dynamic multi-grain parallelism on heterogeneous multi-core processors. Heterogeneous multi-core processors integrate con...
Filip Blagojevic, Dimitrios S. Nikolopoulos, Alexa...