Title :
Competition-based supervised learning algorithm for nonlinear discriminant functions
Author :
Kung, S.Y. ; Mao, W.D.
Author_Institution :
Dept. of Electr. Eng., Princton Univ., NJ, USA
Abstract :
A basic competition-based model is the now-classic perceptron net using linear discriminant functions. The competition-based learning is extended to the general cases of nonlinear discriminant functions. Generalized perceptron learning rules for the binary-classification and multiple-classification cases are proposed. The convergency properties of the general perceptrons are established. Simulation results on texture classification applications are provided
Keywords :
learning systems; neural nets; pattern recognition; binary-classification; competition-based model; convergence properties; generalised perceptron learning rules; multiple-classification; nonlinear discriminant functions; perceptron net; supervised learning algorithm; Labeling; Laser radar; Learning systems; Machine learning; Supervised learning;
Conference_Titel :
Acoustics, Speech, and Signal Processing, 1991. ICASSP-91., 1991 International Conference on
Conference_Location :
Toronto, Ont.
Print_ISBN :
0-7803-0003-3
DOI :
10.1109/ICASSP.1991.150542
