DocumentCode :
3269570
Title :
Algebraic analysis of neural net learning
Author :
Clingman, W.H. ; Friesen, D.K.
Author_Institution :
Prod. Syst. Co., Dallas, TX, USA
fYear :
1989
fDate :
0-0 1989
Abstract :
Summary form only given, as follows. An approach is presented to the algebraic analysis of learning paradigms in neural nets. The technique is to map the learning paradigm into a learning automaton with certain convergence characteristics. Such automata have been studied by the authors, and their algebraic structure was analyzed. From this structure a lower bound can be assigned to the number of steps in a learning sequence. Using the mapping, a similar lower bound can be deduced for the learning paradigm.<>
Keywords :
automata theory; learning systems; neural nets; algebraic analysis; convergence characteristics; learning automaton; learning paradigms; lower bound; mapping; neural net; Automata; Learning systems; Neural networks;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Neural Networks, 1989. IJCNN., International Joint Conference on
Conference_Location :
Washington, DC, USA
Type :
conf
DOI :
10.1109/IJCNN.1989.118513
Filename :
118513
Link To Document :
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