Title :
Neural models and learning rules
Author_Institution :
Lockheed Artificial Intelligence Center, Palo Alto, CA, USA
fDate :
Feb. 29 1988-March 3 1988
Abstract :
The basic computing element in models of neural networks that focus on information processing capabilities is a ´neural´ unit that has an output that is a function of the sum of its inputs. Information is stored in ´synapses´ or connection strengths between units. Networks of these neurons are not programmed like standard computers, but trained by data input. The author examines both unsupervised learning algorithms, which allow networks to find correlations in the input, and supervised learning algorithms, which allow the pairing of arbitrary patterns.<>
Keywords :
learning systems; neural nets; connection strengths; information processing; learning algorithms; learning rules; neural networks; Artificial intelligence; Artificial neural networks; Books; Computer networks; Information processing; Logic; Neural networks; Neurons; Turning; Unsupervised learning;
Conference_Titel :
Compcon Spring '88. Thirty-Third IEEE Computer Society International Conference, Digest of Papers
Conference_Location :
San Francisco, CA, USA
Print_ISBN :
0-8186-0828-5
DOI :
10.1109/CMPCON.1988.4894
