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Combining Multilayer Networks to Combine Learning

Bax, Eric (1997) Combining Multilayer Networks to Combine Learning. California Institute of Technology , Pasadena, CA. (Unpublished) http://resolver.caltech.edu/CaltechCSTR:1997.cs-tr-98-01

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Use this Persistent URL to link to this item: http://resolver.caltech.edu/CaltechCSTR:1997.cs-tr-98-01

Abstract

This paper explores methods to combine networks, implementations of network combination on multicomputers, and applications of network combination. We divide the network combination process into two steps. First, a matching algorithm rearranges the networks so that corresponding weights in the networks being combined play corresponding roles in the networks' functions. Then a proportioning algorithm chooses a convex combination of the matched networks. The combination network is a weight-by-weight convex combination of the matched networks. This paper begins by examining combinations of pairs of networks. Matching and proportioning algorithms are developed, analyzed, implemented, and tested. Next, algorithms are developed to combine several networks. Then the combination process is poked and prodded to explore its nature, its utility, and its limits.


Item Type:Report or Paper (Technical Report)
Additional Information:© 1997 California Institute of Technology. March 28, 1997. I thank Yaser Abu-Mostafa for his advice and guidance concerning the development of these results. I thank Zehra Cataltepe , Malik Magdon-Ismaiel, Joseph Sill, and Xubo Song for many helpful pointers and educational conversations.
Group:Computer Science Technical Reports
DOI:10.7907/Z9MK69XR
Record Number:CaltechCSTR:1997.cs-tr-98-01
Persistent URL:http://resolver.caltech.edu/CaltechCSTR:1997.cs-tr-98-01
Usage Policy:You are granted permission for individual, educational, research and non-commercial reproduction, distribution, display and performance of this work in any format.
ID Code:26831
Collection:CaltechCSTR
Deposited By: Imported from CaltechCSTR
Deposited On:30 Apr 2001
Last Modified:20 Mar 2017 19:26

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