| Abstract
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<p>We present a stochastic learning … <p>We present a stochastic learning algorithm for neural networks. The algorithm does not make any assumptions about transfer functions of individual neurons and does not depend on a functional form of a performance measure. The algorithm uses a random step of varying size to adapt weights. The average size of the step decreases during learning. The large steps enable the algorithm to jump over local maxima/minima, while the small ones ensure convergence in a local area. We investigate convergence properties of the proposed algorithm as well as test the algorithm on four supervised and unsupervised learning problems. We have found a superiority of this algorithm compared to several known algorithms when testing them on generated as well as real data.</p> generated as well as real data.</p>
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| Author
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Antanas Verikas +
, Adas Gelzinis +
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| DOI
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http://dx.doi.org/10.1016/S0925-2312(99)00123-X +
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| Diva
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http://hh.diva-portal.org/smash/record.jsf?searchId=1&pid=diva2:286845
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| EndPage
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172 +
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| Issue
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1-4 +
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| Journal
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Neurocomputing +
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| PublicationType
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Journal Paper +
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| Publisher
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Elsevier +
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| StartPage
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153 +
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| Title
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Training neural networks by stochastic optimisation +
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| Volume
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30 +
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| Year
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2000 +
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| Has queryThis property is a special property in this wiki.
|
Publications:Training neural networks by stochastic optimisation +
, Publications:Training neural networks by stochastic optimisation +
, Publications:Training neural networks by stochastic optimisation +
, Publications:Training neural networks by stochastic optimisation +
, Publications:Training neural networks by stochastic optimisation +
, Publications:Training neural networks by stochastic optimisation +
, Publications:Training neural networks by stochastic optimisation +
, Publications:Training neural networks by stochastic optimisation +
, Publications:Training neural networks by stochastic optimisation +
, Publications:Training neural networks by stochastic optimisation +
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| Categories |
Publication +
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| Modification dateThis property is a special property in this wiki.
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30 September 2016 20:39:08 +
|
