Hybrid wavelet υ-support vector machine and chaotic particle swarm optimization for regression estimation

Authors:
Qi Wu
Affiliations:
Key Laboratory of Measurement and Control of Complex Systems of Engineering, Ministry of Education, Southeast University, Nanjing, Jiangsu 210096, China and School of Hotel and Tourism Management, ...
Venue:
Expert Systems with Applications: An International Journal
Year:
2011

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Abstract

In view of the bad approximate results of the existing support vector (SV) kernel for series influenced by multi-factors in quadratic continuous integral space, combining wavelet theory with kernel technique, a wavelet kernel function is put forward in quadratic continuous integral space. And then, wavelet @n-support vector machine (W @n-SVM) with wavelet kernel is proposed. To seek the optimal parameters of W @n-SVM, embedded chaotic particle swarm optimization (ECPSO) is also proposed to optimize parameters of W @n-SVM. The results of application in car sale estimation show that the estimation approach based on the W @n-SVM and ECPSO is effective and feasible. Compared with the traditional model, W @n-SVM method requires fewer samples and has better estimating precision.