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ECAL
2005
Springer
15 years 5 months ago
The Quantitative Law of Effect is a Robust Emergent Property of an Evolutionary Algorithm for Reinforcement Learning
An evolutionary reinforcement-learning algorithm, the operation of which was not associated with an optimality condition, was instantiated in an artificial organism. The algorithm ...
J. J. McDowell, Zahra Ansari
CEC
2005
IEEE
15 years 1 months ago
A note on the population based incremental learning with infinite population size
In this paper, we study the dynamical properties of the population based incremental learning (PBIL) algorithm when it uses truncation, proportional, and Boltzmann selection schema...
Reza Rastegar, Mohammad Reza Meybodi
CVPR
2000
IEEE
16 years 1 months ago
Optimizing Learning in Image Retrieval
Combining learning with vision techniques in interactive image retrieval has been an active research topic during the past few years. However, existing learning techniques either ...
Yong Rui, Thomas S. Huang
ISCA
2008
IEEE
137views Hardware» more  ISCA 2008»
15 years 6 months ago
Self-Optimizing Memory Controllers: A Reinforcement Learning Approach
Efficiently utilizing off-chip DRAM bandwidth is a critical issue in designing cost-effective, high-performance chip multiprocessors (CMPs). Conventional memory controllers deli...
Engin Ipek, Onur Mutlu, José F. Martí...
VLDB
2005
ACM
126views Database» more  VLDB 2005»
15 years 5 months ago
Consistently Estimating the Selectivity of Conjuncts of Predicates
Cost-based query optimizers need to estimate the selectivity of conjunctive predicates when comparing alternative query execution plans. To this end, advanced optimizers use multi...
Volker Markl, Nimrod Megiddo, Marcel Kutsch, Tam M...