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BMCBI
2006
146views more  BMCBI 2006»
15 years 6 months ago
Optimized Particle Swarm Optimization (OPSO) and its application to artificial neural network training
Background: Particle Swarm Optimization (PSO) is an established method for parameter optimization. It represents a population-based adaptive optimization technique that is influen...
Michael Meissner, Michael Schmuker, Gisbert Schnei...
AAAI
2006
15 years 7 months ago
When a Decision Tree Learner Has Plenty of Time
The majority of the existing algorithms for learning decision trees are greedy--a tree is induced top-down, making locally optimal decisions at each node. In most cases, however, ...
Saher Esmeir, Shaul Markovitch
ICML
2009
IEEE
16 years 6 months ago
Near-Bayesian exploration in polynomial time
We consider the exploration/exploitation problem in reinforcement learning (RL). The Bayesian approach to model-based RL offers an elegant solution to this problem, by considering...
J. Zico Kolter, Andrew Y. Ng
ISCA
2006
IEEE
131views Hardware» more  ISCA 2006»
15 years 12 months ago
Reducing Startup Time in Co-Designed Virtual Machines
A Co-Designed Virtual Machine allows designers to implement a processor via a combination of hardware and software. Dynamic binary translation converts code written for a conventi...
Shiliang Hu, James E. Smith
IFIP13
2004
15 years 7 months ago
A Sampling Model to Ascertain Automation-Induced Complacency in Multi-Task Environments
: This article discusses the development of a model that defines the optimal sampling behaviour of operators in a multi-task flight simulation, where one of the tasks is automated....
Nasrine Bagheri, Greg A. Jamieson