Feature selection using neural networks with contribution measures

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Abstract
There still seems to be a misapprehension that neural networks are capable of dealing with large amounts of noise and useless data. This is true to a certain extent but it is also true that the cleaner and more descriptive the data is the better the neural networks will perform, especially when dealing with small data sets. A method for determining how useful input features are in giving correct classifcations using neural networks is discussed here.
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Author(s)
Milne, Linda
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Publication Year
1995
Resource Type
Conference Paper
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UNSW Faculty
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download milne_ai95.pdf 155.25 KB Adobe Portable Document Format
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