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GECCO
2011
Springer
276views Optimization» more  GECCO 2011»
12 years 11 months ago
Evolution of reward functions for reinforcement learning
The reward functions that drive reinforcement learning systems are generally derived directly from the descriptions of the problems that the systems are being used to solve. In so...
Scott Niekum, Lee Spector, Andrew G. Barto
CEC
2007
IEEE
14 years 1 months ago
Computational chemotaxis in ants and bacteria over dynamic environments
— Chemotaxis can be defined as an innate behavioural response by an organism to a directional stimulus, in which bacteria, and other single-cell or multicellular organisms direct...
Vitorino Ramos, Carlos Fernandes, Agostinho C. Ros...
KDD
2006
ACM
213views Data Mining» more  KDD 2006»
14 years 8 months ago
Learning sparse metrics via linear programming
Calculation of object similarity, for example through a distance function, is a common part of data mining and machine learning algorithms. This calculation is crucial for efficie...
Glenn Fung, Rómer Rosales
MM
2010
ACM
158views Multimedia» more  MM 2010»
13 years 5 months ago
Image segmentation with patch-pair density priors
In this paper, we investigate how an unlabeled image corpus can facilitate the segmentation of any given image. A simple yet efficient multi-task joint sparse representation model...
Xiaobai Liu, Jiashi Feng, Shuicheng Yan, Hai Jin
ICCAD
2003
IEEE
221views Hardware» more  ICCAD 2003»
14 years 4 months ago
Combined Dynamic Voltage Scaling and Adaptive Body Biasing for Heterogeneous Distributed Real-time Embedded Systems
Abstract— Dynamic voltage scaling (DVS) is a powerful technique for reducing dynamic power consumption in a computing system. However, as technology feature size continues to sca...
Le Yan, Jiong Luo, Niraj K. Jha