Document Type

Report

Date

7-1980

Embargo Period

5-1-2012

Keywords

Neural learning

Language

English

Disciplines

Computer Sciences

Description/Abstract

This paper addresses the relationship between the number of hidden layer nodes in a neural network, the complexity of a multi-class discrimination problem, and the number of samples needed for effective learning. Bounds are given for the latter. We show that Ω(min(d,n).M) boundary samples are required for successful classification of M clusters of samples using a 2 hidden layer neural network with d-dimensional inputs and n nodes in the first hidden layer.

Additional Information

School of Information and Computer Science, Syracuse University, SU-CIS-90-20

Source

local

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