Stability of Neural Networks for Slightly Perturbed Training Data Sets

In learning models of artificial neural networks, that randomness comes from the distribution of the training data. We show individual observations do not affect excessively for a neutral network modeling, provided that it has adequate nodes on the hidden layer and proves that the empirical error of...

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Bibliographic Details
Published in:	Communications in statistics. Theory and methods Vol. 33; no. 9; pp. 2259 - 2270
Main Authors:	Berhane, Indrias, Srinivasan, C.
Format:	Journal Article
Language:	English
Published:	Philadelphia, PA Taylor & Francis Group 31.12.2004 Taylor & Francis
Subjects:	Applied sciences Artificial intelligence Artificial neural networks Computer science; control theory; systems Distribution theory Exact sciences and technology Inference from stochastic processes; time series analysis Learning and adaptive systems Markov processes Mathematics Primary 68T99 Probability and statistics Probability theory and stochastic processes Sciences and techniques of general use Secondary 62M45, 68T05 Stability Statistics Learning Statistical method Stability Error estimation Randomness Distribution function Neural network
ISSN:	0361-0926, 1532-415X
Online Access:	Get full text
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