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Stochastic optimization / Measurement / Natural evolution strategy / Fisher information / Expectation–maximization algorithm / CMA-ES / Dimensional analysis / Parametric model / Maximum likelihood / Statistics / Estimation theory / Evolutionary algorithms


Document Date: 2011-08-30 09:40:37

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City

Monte Carlo / Piscataway / Seattle / Los Angeles / Suganthan / Hongkong / Douglas / Montreal / be approximated using Monte Carlo / /

Company

Neural Information Processing Systems / IEEE Press / Neural Networks / MIT Press / bk kG / /

Country

United States / Canada / /

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Facility

Nanyang Technological University / /

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search parameters / multinormal search distribution / search gradients / search gradient / near-optimal solutions / continuous search spaces / stochastic search algorithms / estimated search gradient / natural search gradient / unstable systems / search gradient estimation / computing / higher-dimensional search spaces / information processing systems / search direction / search space / consecutive search distributions / stochastic search / stochastic search methods / learning algorithm / search distribution / search methods / correlated search directions / /

Organization

Congress / MIT / Nanyang Technological University / Singapore / /

Person

Stefan Schaal / Fred Ducatelle / Tom Schaul / /

Position

empirical Fisher information matrix / author / Natural actor-critic / Fisher information matrix / controller / /

Product

Cigar DiffPow Ellipsoid ParabR Schwefel SharpR Sphere Tablet / /

ProgrammingLanguage

Python / /

ProvinceOrState

P. N. / South Carolina / /

PublishedMedium

Machine Learning / /

Technology

resulting algorithm / neural network / NES algorithm / Machine Learning / stochastic search algorithms / /

URL

www.pybrain.org / www.idsia.ch/~sun/enes.html / /

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