Placing spline knots in neural networks using splines as activation functions
Abstract
When using feed-forward neural networks with spline activation functions, the quality of approximation depends on the knot placement of spline functions. We demonstrate a method of choosing equidistant knots in each subdivision of the space when an arbitrary initial division is given, in order to keep the approximation error under a predefined limit.
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- Hlavackova-Schindler, Katerina
- Verleysen, Michel
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Shortfacts
Category |
Journal Paper |
Divisions |
Data Mining and Machine Learning |
Subjects |
Kuenstliche Intelligenz |
Journal or Publication Title |
Neurocomputing |
ISSN |
0925-2312 |
Publisher |
Elsevier |
Page Range |
pp. 159-166 |
Number |
3-4 |
Volume |
17 |
Date |
1997 |
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