Nearest neighbor classification using CMAC

Author

Ramesh, Nagarajan ; Sethi, Ishwar K.

Author_Institution

Dept. of Comput. Sci., Wayne State Univ., Detroit, MI, USA

Volume

5

fYear

1994

fDate

27 Jun-2 Jul 1994

Firstpage

3061

Abstract

In this paper we propose an efficient and flexible method, using the CMAC, to find the nearest neighbor of an input pattern. By searching for the nearest neighbor among a small set of probable candidates, we reduce the number of distance computations compared to the traditional approach. Unlike many other efficient techniques, this system can be trained on additional design patterns at a later time without affecting the previous learning. Experimental results are presented to demonstrate the efficiency of the proposed approach

Keywords

cerebellar model arithmetic computers; computational complexity; optimisation; pattern classification; performance evaluation; CMAC; cerebellar model arithmetic computer; nearest neighbor classification; neural nets; pattern classification; Bayesian methods; Cellular neural networks; Computational complexity; Computer networks; Computer science; Computer vision; Nearest neighbor searches; Neural networks; Pattern classification; Recurrent neural networks;

fLanguage

English

Publisher

ieee

Conference_Titel

Neural Networks, 1994. IEEE World Congress on Computational Intelligence., 1994 IEEE International Conference on

Conference_Location

Orlando, FL

Print_ISBN

0-7803-1901-X

Type

conf

DOI

10.1109/ICNN.1994.374721

Filename

374721