Title :
Complexity of learning: the case of everyday neural networks
Author :
Oláh, B. ; Szepesvári, Cs
Author_Institution :
Jozsef Attila Univ., Szeged, Hungary
fDate :
27 Jun-2 Jul 1994
Abstract :
The authors examine two slightly different domains of the learning problem. They find a polynomial time result, and also an NP-completeness result in these two domains. The domains are chosen so, that commonly used neural network architectures are included
Keywords :
computational complexity; learning (artificial intelligence); neural net architecture; neural nets; NP-completeness; complexity; neural networks; polynomial time result; Artificial neural networks; Computer aided software engineering; Computer architecture; Computer networks; Neural networks; Neurons; Polynomials; Supervised learning; Terminology;
Conference_Titel :
Neural Networks, 1994. IEEE World Congress on Computational Intelligence., 1994 IEEE International Conference on
Conference_Location :
Orlando, FL
Print_ISBN :
0-7803-1901-X
DOI :
10.1109/ICNN.1994.374139
