Scaling kernels: a new least squares support vector machine kernel for approximation

Authors:
Mu Xiangyang;Zhang Taiyi;Zhou Yatong
Affiliations:
Dept. of Information and Commun. Engineering, Xi'an Jiaotong University, Xi'an, China;Dept. of Information and Commun. Engineering, Xi'an Jiaotong University, Xi'an, China;School of Information Engineering, Hebei University of Technology, Tianjin, China
Venue:
MICAI'07 Proceedings of the artificial intelligence 6th Mexican international conference on Advances in artificial intelligence
Year:
2007

Citing 8
Cited 0

A Theory for Multiresolution Signal Decomposition: The Wavelet Representation

IEEE Transactions on Pattern Analysis and Machine Intelligence
An equivalence between sparse approximation and support vector machines

Neural Computation
An introduction to support Vector Machines: and other kernel-based learning methods

An introduction to support Vector Machines: and other kernel-based learning methods
Pattern Classification (2nd Edition)

Pattern Classification (2nd Edition)
Wavelet kernel penalized estimation for non-equispaced design regression

Statistics and Computing
Frames, Reproducing Kernels, Regularization and Learning

The Journal of Machine Learning Research
Wavelet support vector machine

IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
An introduction to kernel-based learning algorithms

IEEE Transactions on Neural Networks

Quantified Score

Hi-index	0.00

Visualization

Abstract

Support vector machines(SVM) have been introduced for pattern recognition and regression. But it was limited by the time consuming and the choice of kernel function in practical application. Motivated by the theory of multi-scale representations of signals and wavelet transforms, this paper presents a way for building a wavelet-based reproducing kernel Hilbert spaces (RKHS) which is a multiresolution scale subspace and its associate scaling kernel for least squares support vector machines (LS-SVM). The scaling kernel is constructed by using a scaling function with its different dilations and translations. Results on several approximation problems illustrate that the LS-SVM with scaling kernel can approximate arbitrary signal with multi-scale and owns better approximation performance.