Feature map learning with partial training data

Tariq Samad; Steven A. Harp

Feature map learning with partial training data

Technological Leadership Institute

Research output: Chapter in Book/Report/Conference proceeding › Conference 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 language	English (US)
Title of host publication	Proceedings. IJCNN - International Joint Conference on Neural Networks
Editors	Anon
Publisher	Publ by IEEE
Number of pages	1
ISBN (Print)	0780301641
State	Published - Jan 1 1992
Event	International Joint Conference on Neural Networks - IJCNN-91-Seattle - Seattle, WA, USA Duration: Jul 8 1991 → Jul 12 1991

Other

Other	International Joint Conference on Neural Networks - IJCNN-91-Seattle
City	Seattle, WA, USA
Period	7/8/91 → 7/12/91

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title = "Feature map learning with partial training data",

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.",

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N2 - 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.

AB - 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.

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M3 - Conference contribution

AN - SCOPUS:0026692312

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BT - Proceedings. IJCNN - International Joint Conference on Neural Networks

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Y2 - 8 July 1991 through 12 July 1991

ER -

Feature map learning with partial training data

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