Approximation using cubic B-splines with improved training speed and accuracy

Approximation using cubic B-splines with improved training speed and accuracy

Abstract

When using cubic B-splines, the quality of approximation depends on the placement of the knots. This paper describes the practical application of a new method for the selection of knot densities. Using a filtering and merging algorithm to split the input space into distinct regions, the number of equidistant knots in each subdivision of the space can be calculated in order to keep the approximation error below a predefined limit. In addition to the smoothing of the error surface, the technique also has the advantage of reducing the computational cost of calculating the spline approximation parameters.

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Authors
  • Mason, Julian D.
  • Schindlerova (Hlavackova-Schindler), Katerina
  • Warwick, Kevin
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Shortfacts
Category
Book Section/Chapter
Divisions
Data Mining and Machine Learning
Subjects
Kuenstliche Intelligenz
Title of Book
Computer Intensive Methods in Control and Signal Processing
Page Range
pp. 295-303
Date
1997
Official URL
https://link.springer.com/chapter/10.1007/978-1-46...
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