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ICPR
2010
IEEE
13 years 8 months ago
Adaptive Incremental Learning with an Ensemble of Support Vector Machines
The incremental updating of classifiers implies that their internal parameter values can vary according to incoming data. As a result, in order to achieve high performance, incre...
Marcelo N. Kapp, Robert Sabourin, Patrick Maupin
TC
2008
13 years 7 months ago
Counter-Based Cache Replacement and Bypassing Algorithms
Recent studies have shown that, in highly associative caches, the performance gap between the Least Recently Used (LRU) and the theoretical optimal replacement algorithms is large,...
Mazen Kharbutli, Yan Solihin
TSMC
2008
138views more  TSMC 2008»
13 years 7 months ago
Fast Diagnosis With Sensors of Uncertain Quality
This correspondence presents an approach to the detection and isolation of component failures in large-scale systems. In the case of sensors that report at rates of 1 Hz or less, t...
Ozgur Erdinc, Craig Brideau, Peter Willett, Thiaga...
TMI
2010
151views more  TMI 2010»
13 years 2 months ago
Quantitative Analysis of Pulmonary Emphysema Using Local Binary Patterns
Abstract--We aim at improving quantitative measures of emphysema in computed tomography (CT) images of the lungs. Current standard measures, such as the relative area of emphysema ...
Lauge Sørensen, Saher B. Shaker, Marleen de...
GECCO
2009
Springer
141views Optimization» more  GECCO 2009»
14 years 2 months ago
Distributed hyper-heuristics for real parameter optimization
Hyper-heuristics (HHs) are heuristics that work with an arbitrary set of search operators or algorithms and combine these algorithms adaptively to achieve a better performance tha...
Marco Biazzini, Balázs Bánhelyi, Alb...