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

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 ν-support vector machine (W ν-SVM) with wavelet kernel is proposed. To seek the optimal parameters of W ν-SVM, embedded chaotic particle swarm optimization (ECPSO) is also proposed to optimize parameters of W ν-SVM. The results of application in car sale estimation show that the estimation approach based on the W ν-SVM and ECPSO is effective and feasible. Compared with the traditional model, W ν-SVM method requires fewer samples and has better estimating precision.