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Neural networks / Science / Multivariate statistics / Artificial intelligence / Backpropagation through time / Recurrent neural network / Nonlinear dimensionality reduction / Machine learning / Kalman filter / Statistics / Computational neuroscience / Cybernetics


Document Date: 2011-08-08 16:36:57

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New York / Hillsdale / San Jose / /

Company

Neural Systems / Neural Information Processing Systems / Neural Networks / MIT Press / ACM Press / IEEE Press / Lawrence Erlbaum Associates / SEIT / Hill / /

Country

United States / /

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USD / /

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Facility

Brigham Young University / Cambridge University / /

IndustryTerm

path search algorithms / problem-specific sensory devices / nonlinear dynamical systems / real-coded genetic algorithm / dynamic error propagation network / supervised learning algorithms / dynamical systems / greedy path search / recurrent networks / greedy algorithm / inefficient solution / supervised learning regression algorithm / backpropagation algorithm / real-world systems / /

Organization

Cambridge University / Engineering Department / MIT / National Academy of Sciences / Brigham Young University / Department of Computer Science / /

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Evolutionary / Tony Martinez Abstract / Mike Gashler / Tony Martinez / J. B. Tenenbaum / V / /

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Hill climber / feed-forward / climber / /

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Isomap / /

ProvinceOrState

California / /

PublishedMedium

Machine Learning / /

Technology

Three algorithms / supervised learning regression algorithm / regular NLDR algorithm / supervised learning algorithms / corresponding NLDR algorithms / regular NLDR algorithms / Machine Learning / 5 algorithms / path search algorithms / D. Some NLDR algorithms / real-coded genetic algorithm / neural network / NLDR algorithm / existing NLDR algorithms / Existing algorithms / NLDR algorithms / greedy algorithm / backpropagation algorithm / five algorithms / /

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