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» Model Selection and Error Estimation
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ICANN
2009
Springer
15 years 9 months ago
Selective Attention Improves Learning
Abstract. We demonstrate that selective attention can improve learning. Considerably fewer samples are needed to learn a source separation problem when the inputs are pre-segmented...
Antti Yli-Krekola, Jaakko Särelä, Harri ...
CVPR
2006
IEEE
16 years 4 months ago
A Framework for Feature Selection for Background Subtraction
Background subtraction is a widely used paradigm to detect moving objects in video taken from a static camera and is used for various important applications such as video surveill...
Toufiq Parag, Ahmed M. Elgammal, Anurag Mittal
MICRO
2006
IEEE
127views Hardware» more  MICRO 2006»
15 years 8 months ago
A Predictive Performance Model for Superscalar Processors
Designing and optimizing high performance microprocessors is an increasingly difficult task due to the size and complexity of the processor design space, high cost of detailed si...
P. J. Joseph, Kapil Vaswani, Matthew J. Thazhuthav...
123
Voted
ICC
2009
IEEE
124views Communications» more  ICC 2009»
15 years 9 days ago
Optimal Weighted Antenna Selection for Imperfect Channel Knowledge from Training
Receive antenna selection (AS) reduces the hardware complexity of multi-antenna receivers by dynamically connecting an instantaneously best antenna element to the available radio ...
Vinod Kristem, Neelesh B. Mehta, Andreas F. Molisc...
BMCBI
2011
14 years 9 months ago
Systematic error detection in experimental high-throughput screening
Background: High-throughput screening (HTS) is a key part of the drug discovery process during which thousands of chemical compounds are screened and their activity levels measure...
Plamen Dragiev, Robert Nadon, Vladimir Makarenkov