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Neural networks / Matrices / Multivariable calculus / Singular value decomposition / Design of experiments / Fisher information / Hessian matrix / Multilayer perceptron / Artificial neuron / Algebra / Mathematics / Linear algebra


On practical use of stagewise second-order backpropagation for multi-stage neural-network learning Eiji Mizutani and Stuart Dreyfus Abstract— We analyze the Hessian matrix H of the sumsquared-error measure for multilay
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Document Date: 2008-07-16 21:59:52

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Orlando / Taipei / New York / Oxford / Washington / D.C. / Berlin / Portland / Como / Vancouver / Montreal Quebec / New-York / /

Company

Neural Information Processing Systems / BP / Neural Networks / MIT Press / Mathematical Software / John Wiley & Sons Inc. / Epoch Epoch (d) Trust / /

Country

Taiwan / Italy / United States / Canada / /

Currency

pence / / /

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Facility

University of California at Berkeley / Prentice Hall / Rice University / National Taiwan University of Science / /

IndustryTerm

structure-exploiting trustregion algorithm / backpropagation algorithms / learning algorithms / numerical solution / /

Organization

University of California / MIT / UC Berkeley / Rice University / U.S. Securities and Exchange Commission / National Taiwan University / SD Trust / Department of Industrial Engineering and Operations Research / Department of Industrial Management / /

Person

Carey Fan / James W. Demmel / Philippe L. Toint / Richard O. Duda / Christopher M. Bishop / Stuart E. Dreyfus / John Dennis / Jing-Yun Carey / John E. Dennis / James Demmel / Guian Zhou / Eiji Mizutani / Andrew R. Conn / Jennie Si / Robert B. Schnabel / /

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Position

editor / Fisher information matrix / /

Product

F15 / F22 / /

ProgrammingLanguage

MATLAB / /

ProvinceOrState

New Jersey / Oregon / New York / /

Technology

Lanczos algorithms / existing learning algorithms / Secondorder backpropagation algorithms / neural network / structure-exploiting trustregion algorithm / Stagewise algorithm / simulation / /

URL

www.netlib.org / /

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