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» Distributed Machine Learning: Scaling Up with Coarse-grained...
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MLDM
2009
Springer
14 years 3 months ago
PMCRI: A Parallel Modular Classification Rule Induction Framework
In a world where massive amounts of data are recorded on a large scale we need data mining technologies to gain knowledge from the data in a reasonable time. The Top Down Induction...
Frederic T. Stahl, Max A. Bramer, Mo Adda
ICDCS
2002
IEEE
14 years 1 months ago
A Fully Distributed Framework for Cost-Sensitive Data Mining
Data mining systems aim to discover patterns and extract useful information from facts recorded in databases. A widely adopted approach is to apply machine learning algorithms to ...
Wei Fan, Haixun Wang, Philip S. Yu, Salvatore J. S...
WAN
1998
Springer
14 years 4 hour ago
Performance Analysis of Wavefront Algorithms on Very-Large Scale Distributed Systems
We present a model for the parallel performance of algorithms that consist of concurrent, two-dimensional wavefronts implemented in a message passing environment. The model combine...
Adolfy Hoisie, Olaf M. Lubeck, Harvey J. Wasserman
CLUSTER
2007
IEEE
14 years 11 days ago
Identifying energy-efficient concurrency levels using machine learning
Abstract-- Multicore microprocessors have been largely motivated by the diminishing returns in performance and the increased power consumption of single-threaded ILP microprocessor...
Matthew Curtis-Maury, Karan Singh, Sally A. McKee,...
CLOUDCOM
2010
Springer
13 years 6 months ago
Scaling Populations of a Genetic Algorithm for Job Shop Scheduling Problems Using MapReduce
Inspired by Darwinian evolution, a genetic algorithm (GA) approach is one of the popular heuristic methods for solving hard problems, such as the Job Shop Scheduling Problem (JSSP...
Di-Wei Huang, Jimmy Lin