Self-organization with partial data

Tariq Samad, Steven A. Harp

Research output: Contribution to journalArticlepeer-review

79 Scopus citations

Abstract

We show how the kohonen self-organizing feature map model can be extended so that partial training data can be utilized. Given input stimuli in which values for some elements or features are absent, the match computation and the weight updates are performed in the input subspace defined by the available values. Three examples, including an application to student modelling for intelligent tutoring systems in which data is inherently incomplete, demonstrate the effectiveness of the extension.

Original languageEnglish (US)
Pages (from-to)205-212
Number of pages8
JournalNetwork: Computation in Neural Systems
Volume3
Issue number2
DOIs
StatePublished - 1992

Bibliographical note

Funding Information:
This research was supported in part by the AF Armstrong Laboratory, Human Resource Directorate, United States Air Force, Brooks AFB, TX 78235-5601.

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