Biehl, Michael

Marangi, Carmela

Riegler, Peter

Solla, Sara A.

English

We analyse on-line learning of a linearly separable rule with a simple perceptron. Example inputs are taken from two overlapping clusters of data and the rule is defined through a teacher vector which is in general not aligned with the connection line of the cluster centers. We find that the Hebb algorithm cannot learn the rule perfectly in general. Moreover the dependence of the generalization error on the number of examples is nonmonotonic for certain choices of the model parameters. Perceptron and AdaTron training, however, approach perfect generalization with increasing size of the training set, and the asymptotic behavior is the same as for unstructured input data

Proceedings - University of Groningen

   University of GroningenOn-line learning from clustered input examplesRiegler, Peter; Biehl, Michael; Solla, Sara A.; Marangi, CarmelaPublished in:Proc. 7th Italian Workshop on Neural Networks WIRN 1995IMPORTANT NOTE: You are advised to consult the publisher's version (publisher's PDF) if you wish to cite fromit. Please check the document version below.Document VersionPublisher's PDF, also known as Version of recordPublication date:1996Link to publication in University of Groningen/UMCG research databaseCitation for published version (APA):Riegler, P., Biehl, M., Solla, S. A., & Marangi, C. (1996). On-line learning from clustered input examples. InM. Marinaro, & R. Tagliaferri (Eds.), Proc. 7th Italian Workshop on Neural Networks WIRN 1995 (pp. 87-92). World Scientific Publishing.CopyrightOther than for strictly personal use, it is not permitted to download or to forward/distribute the text or part of it without the consent of theauthor(s) and/or copyright holder(s), unless the work is under an open content license (like Creative Commons).The publication may also be distributed here under the terms of Article 25fa of the Dutch Copyright Act, indicated by the “Taverne” license.More information can be found on the University of Groningen website: https://www.rug.nl/library/open-access/self-archiving-pure/taverne-amendment.Take-down policyIf you believe that this document breaches copyright please contact us providing details, and we will remove access to the work immediatelyand investigate your claim.Downloaded from the University of Groningen/UMCG research database (Pure): http://www.rug.nl/research/portal. For technical reasons thenumber of authors shown on this cover page is limited to 10 maximum.Download date: 11-12-2022

On-line learning from clustered input examples

https://pure.rug.nl/ws/files/3249679/1996ProcWIRNRiegler.pdf

On-line learning from clustered input examples

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Proceedings - University of Groningen