Title :
Learning algorithms for extended models of Boltzmann machines
Author_Institution :
CMLA-DIAM, Ecole Normale Superieure, Cachan, France
Abstract :
We present and study learning rules for specific models of Boltzmann machines which extend classical sequential and reversible synchronous models
Keywords :
Boltzmann machines; Boltzmann machines; extended models; learning algorithms; learning rules; reversible synchronous models; sequential synchronous models; Automated highways; Los Angeles Council; Machine learning; Neural networks; Neurons; Probability distribution; Random variables; Stochastic processes;
Conference_Titel :
Pattern Recognition, 1994. Vol. 2 - Conference B: Computer Vision & Image Processing., Proceedings of the 12th IAPR International. Conference on
Conference_Location :
Jerusalem
Print_ISBN :
0-8186-6270-0
DOI :
10.1109/ICPR.1994.577053
