Feature map learning with partial training data

Tariq Samad, Steven A. Harp

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Summary form only given, as follows. The authors discuss a straightforward extension of the Kohonen self-organizing feature map that permits training and operation with incomplete training examples--input vectors in which values for some elements are missing. The matching and weight updating process is performed in the input subspace defined by the available input values. Three examples demonstrated the effectiveness of the extension.

Original languageEnglish (US)
Title of host publicationProceedings. IJCNN - International Joint Conference on Neural Networks
Editors Anon
PublisherPubl by IEEE
Number of pages1
ISBN (Print)0780301641
StatePublished - Jan 1 1992
EventInternational Joint Conference on Neural Networks - IJCNN-91-Seattle - Seattle, WA, USA
Duration: Jul 8 1991Jul 12 1991

Other

OtherInternational Joint Conference on Neural Networks - IJCNN-91-Seattle
CitySeattle, WA, USA
Period7/8/917/12/91

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  • Cite this

    Samad, T., & Harp, S. A. (1992). Feature map learning with partial training data. In Anon (Ed.), Proceedings. IJCNN - International Joint Conference on Neural Networks Publ by IEEE.