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Neural Computing and Applications

Erschienen in:

01.11.2014 | Original Article

Wavelet twin support vector machine

verfasst von: Shifei Ding, Fulin Wu, Zhongzhi Shi

Erschienen in: Neural Computing and Applications | Ausgabe 6/2014

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Abstract

Twin support vector machine (TWSVM) is a research hot spot in the field of machine learning in recent years. Although its performance is better than traditional support vector machine (SVM), the kernel selection problem still affects the performance of TWSVM directly. Wavelet analysis has the characteristics of multivariate interpolation and sparse change, and it is suitable for the analysis of local signals and the detection of transient signals. The wavelet kernel function based on wavelet analysis can approximate any nonlinear functions. Based on the wavelet kernel features and the kernel function selection problem, wavelet twin support vector machine (WTWSVM) is proposed by this paper. It introduces the wavelet kernel function into TWSVM to make the combination of wavelet analysis techniques and TWSVM come true. The experimental results indicate that WTWSVM is feasible, and it improves the classification accuracy and generalization ability of TWSVM significantly.

Vorheriger Artikel Linear model-based estimation of blood pressure and cardiac output for Normal and Paranoid cases

Nächster Artikel Adaptive wave variables for bilateral teleoperation using neural networks

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Titel: Wavelet twin support vector machine
verfasst von: Shifei Ding
Fulin Wu
Zhongzhi Shi
Publikationsdatum: 01.11.2014
Verlag: Springer London
Erschienen in: Neural Computing and Applications / Ausgabe 6/2014
Print ISSN: 0941-0643
Elektronische ISSN: 1433-3058
DOI: https://doi.org/10.1007/s00521-014-1596-y

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