Title :
Nearest neighbor classification using CMAC
Author :
Ramesh, Nagarajan ; Sethi, Ishwar K.
Author_Institution :
Dept. of Comput. Sci., Wayne State Univ., Detroit, MI, USA
fDate :
27 Jun-2 Jul 1994
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;
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
DOI :
10.1109/ICNN.1994.374721
