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» Adaptive Algorithms for Online Decision Problems
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145
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JSSPP
2009
Springer
15 years 10 months ago
Competitive Two-Level Adaptive Scheduling Using Resource Augmentation
Abstract. As multi-core processors proliferate, it has become more important than ever to ensure efficient execution of parallel jobs on multiprocessor systems. In this paper, we s...
Hongyang Sun, Yangjie Cao, Wen-Jing Hsu
169
Voted
ML
2010
ACM
135views Machine Learning» more  ML 2010»
14 years 10 months ago
Multi-domain learning by confidence-weighted parameter combination
State-of-the-art statistical NLP systems for a variety of tasks learn from labeled training data that is often domain specific. However, there may be multiple domains or sources o...
Mark Dredze, Alex Kulesza, Koby Crammer
138
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GECCO
2010
Springer
191views Optimization» more  GECCO 2010»
15 years 3 months ago
Fitness importance for online evolution
To complement standard fitness functions, we propose "Fitness Importance" (FI) as a novel meta-heuristic for online learning systems. We define FI and show how it can be...
Philip Valencia, Raja Jurdak, Peter Lindsay
CVPR
2010
IEEE
15 years 11 months ago
On-line Semi-supervised Multiple-Instance Boosting
A recent dominating trend in tracking called tracking-by-detection uses on-line classifiers in order to redetect objects over succeeding frames. Although these methods usually deli...
Bernhard Zeisl, Christian Leistner, Amir Saffari, ...
146
Voted
INFOCOM
1998
IEEE
15 years 7 months ago
A Stochastic Approximation Approach for Max-Min Fair Adaptive Rate Control of ABR Sessions with MCRs
The ABR sessions in an ATM network share the bandwidth left over after guaranteeing service to CBR and VBR traffic. Hence the bandwidth available to ABR sessions is randomly varyi...
Santosh Paul Abraham, Anurag Kumar