Title :
Characteristics of a deterministic supervised learning scheme
Author :
Hu, Chia-lun J. ; Tan, Jeng Yoong
Author_Institution :
Dept. of Electr. Eng., Southern Illinois Univ., Carbondale, IL, USA
Abstract :
Supervised learning in a one-layered, hard-limited perceptron can be formulated into a set of linear inequalities containing the unknown weight coefficients. Solving these inequalities under a generalized separability condition by a noniterative method then achieves the goal of supervised learning. The speed of learning, the robustness of recognition, the automatic feature competition and automatic feature extraction, and other characteristics of this novel one-step learning system are then discussed in detail
Keywords :
feature extraction; learning (artificial intelligence); perceptrons; automatic feature competition; automatic feature extraction; deterministic supervised learning scheme; generalized separability condition; linear inequalities; noniterative method; one-layered hard-limited perceptron; one-step learning system; recognition robustness; weight coefficients; Character recognition; Equations; Feature extraction; Linear matrix inequalities; Neural networks; Pattern recognition; Robustness; Supervised learning; Vectors; Virtual manufacturing;
Conference_Titel :
Circuits and Systems, 1993., Proceedings of the 36th Midwest Symposium on
Conference_Location :
Detroit, MI
Print_ISBN :
0-7803-1760-2
DOI :
10.1109/MWSCAS.1993.343054
