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Proceedings Paper

Approximate k-nearest neighbor method
Author(s): Sirpa Saarinen; George Cybenko
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Paper Abstract

Memory based techniques are becoming increasingly popular as learning methods. The k- nearest neighbor method has often been mentioned as one of the best learning methods but it has two basic drawbacks: the large storage demand and the often tedious search of the neighbors. In this paper, we present a method for approximating k-th nearest neighbor methods by using a hybrid kernel function and explicit data representation and thus reducing the amount of data used. This method will not use the correct nearest neighbors to a point but will use an average measure of them. Finding the real neighbors is not always needed for accurate classification but finding a few nearby points is sufficient for most cases.

Paper Details

Date Published: 1 September 1993
PDF: 8 pages
Proc. SPIE 1962, Adaptive and Learning Systems II, (1 September 1993); doi: 10.1117/12.150579
Show Author Affiliations
Sirpa Saarinen, Univ. of Illinois/Urbana-Champaign (United States)
George Cybenko, Dartmouth College (United States)


Published in SPIE Proceedings Vol. 1962:
Adaptive and Learning Systems II
Firooz A. Sadjadi, Editor(s)

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