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KDD
1999
ACM
104views Data Mining» more  KDD 1999»
15 years 2 months ago
Learning Rules from Distributed Data
In this paper a concern about the accuracy (as a function of parallelism) of a certain class of distributed learning algorithms is raised, and one proposed improvement is illustrat...
Lawrence O. Hall, Nitesh V. Chawla, Kevin W. Bowye...
ICPP
1996
IEEE
15 years 1 months ago
Polynomial-Time Nested Loop Fusion with Full Parallelism
Data locality and synchronization overhead are two important factors that affect the performance of applications on multiprocessors. Loop fusion is an effective way for reducing s...
Edwin Hsing-Mean Sha, Chenhua Lang, Nelson L. Pass...
SIAMCO
2010
128views more  SIAMCO 2010»
14 years 8 months ago
A Parallel Splitting Method for Coupled Monotone Inclusions
A parallel splitting method is proposed for solving systems of coupled monotone inclusions in Hilbert spaces, and its convergence is established under the assumption that solutions...
Hedy Attouch, Luis M. Briceño-Arias, Patric...
FTTCS
2006
132views more  FTTCS 2006»
14 years 9 months ago
Algorithms and Data Structures for External Memory
Data sets in large applications are often too massive to fit completely inside the computer's internal memory. The resulting input/output communication (or I/O) between fast ...
Jeffrey Scott Vitter
ICDCS
2000
IEEE
15 years 2 months ago
The Effect of Nogood Learning in Distributed Constraint Satisfaction
We present resolvent-based learning as a new nogood learning method for a distributed constraint satisfaction algorithm. This method is based on a look-back technique in constrain...
Makoto Yokoo, Katsutoshi Hirayama