In this paper, Nu-support vector regression is employed as method to model time series prediction; data set preprocessing method is proposed to transform original time series to standardized set of labeled samples; in order to optimize parameters of prediction model, two-stage search strategy is proposed based on parallelization and granularity control.

Using support vector regression for QSPR of herbicidal l,4,2-diazaphospholidin-5-(thi) one-2-oxides;

Conical thread detection based on support vector regression;

UV spectrometry and support vector regression for simultaneous determination of paracetamol,aspirin and caffeine;

Nonlinear combined prediction of port throughput based on support vector machine regression;

To improve the predication precision in quantitative structure-activity relationship(QSAR) research,a novel nonlinear combinatorial forecast method based on support vector machine regression and k-near neighbor group was proposed.

Based on the support vector machine regression approximating the limit state function,a procedure is presented to analyze the reliability and corresponding sensitivity of the structural system.

Using support vector regression for QSAR study on the aldose reductase inhibitory activity of some flavones;

A recommendation algorithm based on Support Vector Regression;

GPS elevation transform model based on least squares support vector regression;

Harmonics analysis based on support vector regression machine;

Gyro drift prediction model based on support vector regression machine;

Research on structural health monitoring for wing box based on support vector regression machine

Considering the favorable performance of support vector machines (SVM) in the respect of machine learning, a seepage-monitoring model based on support vector regression (SVR) is put forward.

This new method is applied in the study of multimedia interaction and in predicting the mouth movement by speech based on support vector regression (SVR) .