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PLDI
2003
ACM
16 years 20 days ago
A comparison of empirical and model-driven optimization
Empirical program optimizers estimate the values of key optimization parameters by generating different program versions and running them on the actual hardware to determine which...
Kamen Yotov, Xiaoming Li, Gang Ren, Michael Cibuls...
216
Voted
SMC
2007
IEEE
102views Control Systems» more  SMC 2007»
16 years 1 months ago
An improved immune Q-learning algorithm
—Reinforcement learning is a framework in which an agent can learn behavior without knowledge on a task or an environment by exploration and exploitation. Striking a balance betw...
Zhengqiao Ji, Q. M. Jonathan Wu, Maher A. Sid-Ahme...
GECCO
2008
Springer
145views Optimization» more  GECCO 2008»
15 years 8 months ago
An evolutionary approach for competency-based curriculum sequencing
The process of creating e-learning contents using reusable learning objects (LOs) can be broken down in two sub-processes: LOs finding and LO sequencing. Sequencing is usually per...
Luis de Marcos, José-Javier Martínez...
ICCV
2007
IEEE
16 years 9 months ago
Boosting Invariance and Efficiency in Supervised Learning
In this paper we present a novel boosting algorithm for supervised learning that incorporates invariance to data transformations and has high generalization capabilities. While on...
Andrea Vedaldi, Paolo Favaro, Enrico Grisan
KR
1992
Springer
15 years 11 months ago
Learning Useful Horn Approximations
While the task of answering queries from an arbitrary propositional theory is intractable in general, it can typicallybe performed e ciently if the theory is Horn. This suggests t...
Russell Greiner, Dale Schuurmans