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» Computing LTS Regression for Large Data Sets
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ICS
2005
Tsinghua U.
14 years 2 months ago
The implications of working set analysis on supercomputing memory hierarchy design
Supercomputer architects strive to maximize the performance of scientific applications. Unfortunately, the large, unwieldy nature of most scientific applications has lead to the...
Richard C. Murphy, Arun Rodrigues, Peter M. Kogge,...
GECCO
2006
Springer
162views Optimization» more  GECCO 2006»
14 years 26 days ago
Evolutionary learning with kernels: a generic solution for large margin problems
In this paper we embed evolutionary computation into statistical learning theory. First, we outline the connection between large margin optimization and statistical learning and s...
Ingo Mierswa
JCB
2002
70views more  JCB 2002»
13 years 8 months ago
Strong Feature Sets from Small Samples
For small samples, classi er design algorithms typically suffer from over tting. Given a set of features, a classi er must be designed and its error estimated. For small samples, ...
Seungchan Kim, Edward R. Dougherty, Junior Barrera...
ISCAICIS
2000
13 years 10 months ago
Computer Vision Framework for Analyzing Projections from Video of Lectures
The overhead and computer projectors have become an essential element to the classroom and corporate settings. Users of web-based classes and conferences would like to have access...
Michael N. Wallick, Niels da Vitoria Lobo, Mubarak...
ICASSP
2010
IEEE
13 years 9 months ago
Image-quality prediction of synthetic aperture sonar imagery
This work exploits several machine-learning techniques to address the problem of image-quality prediction of synthetic aperture sonar (SAS) imagery. The objective is to predict th...
David P. Williams