Title :
Capacity and VC-dimension of multilayer network with higher order input transformation
Author :
Kowalczyk, Adam ; Szymanski, Jacek
Author_Institution :
Telecom Australia Res. Labs., Clayton, Vic., Australia
fDate :
29 Nov-2 Dec 1994
Abstract :
The paper investigates a multilayer perceptron built of linear threshold units superposed on a higher order transformation of input, a type of structure considered for optical implementation. Theoretical estimates of its separating capacity, VC-dimension and probability of implementation of a random dichotomy are given. It is shown that the capacity is limited by the number of neurons in the first hidden layer and that for a sufficiently large network it can be lower than the VC-dimension
Keywords :
multilayer perceptrons; optical neural nets; probability; VC-dimension; hidden layer; higher order input transformation; higher order transformation; linear threshold units; multilayer network capacity; multilayer perceptron; optical implementation; random dichotomy; separating capacity; Holographic optical components; Holography; Neural networks; Neurons; Nonhomogeneous media; Nonlinear optics; Optical computing; Optical network units; Polynomials; Zinc;
Conference_Titel :
Intelligent Information Systems,1994. Proceedings of the 1994 Second Australian and New Zealand Conference on
Conference_Location :
Brisbane, Qld.
Print_ISBN :
0-7803-2404-8
DOI :
10.1109/ANZIIS.1994.396961
