Share Email Print

Proceedings Paper

Approximate k-nearest neighbor method
Author(s): Sirpa Saarinen; George Cybenko
Format Member Price Non-Member Price
PDF $14.40 $18.00
cover GOOD NEWS! Your organization subscribes to the SPIE Digital Library. You may be able to download this paper for free. Check Access

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)

© SPIE. Terms of Use
Back to Top