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Randomized Neural Networks for Learning Stochastic Dependences

Borkar, VS and Gupta, Piyush (1999) Randomized Neural Networks for Learning Stochastic Dependences. In: IEEE Transactions on Systems Man and Cybernetics Part B Cybernetics, 29 (4). pp. 469-480.


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We consider the problem of learning the dependence of one random variable on another, from a finite string of independently identically distributed (i.i.d.) copies of the pair. The problem is first converted to that bf learning a function of the latter random variable and an independent random variable uniformly distributed on the unit interval, However, this cannot be achieved using the usual function learning techniques because the samples of the uniformly distributed random variables are not available. We propose a novel loss function, the minimizer of which results in an approximation to the needed function. Through successive approximation results (suggested by the proposed loss function), a suitable class of functions represented by combination feedforward neural networks is selected as the class to learn from. These results are also extended for countable as well as continuous state-space Markov chains. The effectiveness of the proposed method is indicated through simulation studies.

Item Type: Journal Article
Publication: IEEE Transactions on Systems Man and Cybernetics Part B Cybernetics
Publisher: IEEE
Additional Information: ©1999 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.
Keywords: markov chains;randomized neural networks;stochastic dependences
Department/Centre: Division of Electrical Sciences > Computer Science & Automation
Date Deposited: 25 Aug 2008
Last Modified: 27 Feb 2019 10:21
URI: http://eprints.iisc.ac.in/id/eprint/1545

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