Title :
A mixed stochastic optimization algorithm and its applications in pattern recognition
Author_Institution :
Osaka University, Japan
Abstract :
The problem of the minimization of a functional of several parameters is treated. It is supposed that the gradient of the functional is known but the stochastic approximation method cannot be applied since its convergence is not guaranteed due to the form of the functional. A mixed random search-stochastic approximation method insuring this convergence is developed. Several applications in the field of pattern recognition are highlighted.
Keywords :
Approximation algorithms; Approximation methods; Automata; Automatic control; Convergence; Feature extraction; Minimization methods; Pattern recognition; Space stations; Stochastic processes;
Conference_Titel :
Decision and Control including the 12th Symposium on Adaptive Processes, 1973 IEEE Conference on
Conference_Location :
San Diego, CA, USA
DOI :
10.1109/CDC.1973.269190
