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Published in:
2006 | OriginalPaper | Chapter
Artificial Neural Network Resistance to Incomplete Data
Author : Magdalena Alicja Tkacz
Published in: Intelligent Information Processing and Web Mining
Publisher: Springer Berlin Heidelberg
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This paper presents results obtained in experiments related to artificial neural networks. Artificial neural networks have been trained with delta-bar-delta and conjugate gradient algorithms in case of removing some data from dataset and fulfilling empty places with mean. The goal of the experiment was to observe how long will neural network (trained with specific algorithm) be able to learn when dataset will be consistently less and less exact – the number of incomplete data is increased.