Input-Output OOMs

  • Input-output OOMs (IO-OOMs) are an extension of the basic OOM theory to deal with controlled stochastic processes. They were first presented in a comprehensive OOM tutorial text, where an early version of the OOM learning algorithm was partially transferred. Since then, OOM research has focussed on basic OOMs, in particular on developing statistically efficient learning algorithms, and research on IO-OOMs has been dormant. In this report it is shown how the main theorems for OOMs can be transferred to the case of IO-OOMs and how one of the current OOM learning algorithms can be adapted to learning IO-OOMs, yielding the first complete IO-OOM learning algorithm.

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Publishing Institution:IRC-Library, Information Resource Center der Jacobs University Bremen
Author:Michael Thon
Persistent Identifier (URN):urn:nbn:de:gbv:579-opus-1006761
Series (No.):Jacobs University Technical Reports (16)
Document Type:Technical Report
Date of First Publication:2008/05/01
School:SES School of Engineering and Science
Library of Congress Classification:Q Science / QA Mathematics (incl. computer science) / QA71-90 Instruments and machines / QA75.5-76.95 Electronic computers. Computer science / QA76.87 Neural computers. Neural networks

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