Title :
Experience with adaptive probabilistic neural networks and adaptive general regression neural networks
Author :
Specht, Donald F. ; Romsdahl, Harlan
Author_Institution :
Lockheed Palo Alto Res. Lab., CA, USA
fDate :
27 Jun-2 Jul 1994
Abstract :
By adapting separate smoothing parameters for each dimension, the classification accuracy of the the probabilistic neural network (PNN), and the estimation accuracy of the general regression neural network (GRNN) can both be greatly improved. Accuracy comparisons are given for 28 databases. In addition, the dimensionality of the problem and the complexity of the network can usually be simultaneously reduced. The price to be paid for these benefits is increased training time
Keywords :
estimation theory; learning (artificial intelligence); neural nets; pattern classification; smoothing methods; accuracy comparisons; adaptive general regression neural networks; adaptive probabilistic neural networks; classification accuracy; dimensionality; estimation accuracy; smoothing parameters; Adaptive systems; IP networks; Kernel; Laboratories; Missiles; Neural networks; Probability density function; Shape; Smoothing methods; Spatial databases;
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.1994.374355
