Title :
Even simple neural nets cannot be trained reliably with a polynomial number of examples
Author_Institution :
SRI Int., Princeton, NJ, USA
Abstract :
A variation of L.G. Valiant´s ´PAC´ model of learnability (Commun. ACM, vol.27, no.11, p.1134-42, 1984; Proc. 9th Int. Joint Conf. Artif. Intell., Aug. 1985) is used to investigate the learning power of artificial neural nets with threshold nodes. It is shown that there are cases where simple nets require an exponential number of training examples for reliably determining their sets of parameters. Polynomially many training examples may not be enough to determine the set of parameters even for a net of three threshold nodes, if it has to perform reliably in two different environments.<>
Keywords :
learning systems; neural nets; artificial neural nets; learning power; threshold nodes; training examples; Learning systems; Neural networks;
Conference_Titel :
Neural Networks, 1989. IJCNN., International Joint Conference on
Conference_Location :
Washington, DC, USA
DOI :
10.1109/IJCNN.1989.118691
