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104
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JMM2
2007
96views more  JMM2 2007»
15 years 1 months ago
A Framework for Linear Transform Approximation Using Orthogonal Basis Projection
—This paper aims to develop a novel framework to systematically trade-off computational complexity with output distortion in linear multimedia transforms, in an optimal manner. T...
Yinpeng Chen, Hari Sundaram
117
Voted
SIAMSC
2011
153views more  SIAMSC 2011»
14 years 8 months ago
Stable Computations with Gaussian Radial Basis Functions
Abstract. Radial basis function (RBF) approximation is an extremely powerful tool for representing smooth functions in non-trivial geometries, since the method is meshfree and can ...
Bengt Fornberg, Elisabeth Larsson, Natasha Flyer
104
Voted
ADCM
2007
155views more  ADCM 2007»
15 years 1 months ago
Numerical differentiation by radial basis functions approximation
Based on radial basis functions approximation, we develop in this paper a new computational algorithm for numerical differentiation. Under an a priori and an a posteriori choice r...
T. Wei, Y. C. Hon
117
Voted
CVPR
2007
IEEE
16 years 3 months ago
A Variational Approach to the Evolution of Radial Basis Functions for Image Segmentation
In this paper we derive differential equations for evolving radial basis functions (RBFs) to solve segmentation problems. The differential equations result from applying variation...
Greg G. Slabaugh, Huong Quynh Dinh, Gozde B. Unal
115
Voted
ESANN
2001
15 years 2 months ago
Learning fault-tolerance in Radial Basis Function Networks
This paper describes a method of supervised learning based on forward selection branching. This method improves fault tolerance by means of combining information related to general...
Xavier Parra, Andreu Català