The application of the LS-SVM method for the analysis of LIDAR and Motorcycle samples
In paper considers the application of the support vector machines with a quadratic loss function (LS-SVM) for solving practical problems. The analysis of known samples LIDAR and Motorcycle is considered. A brief description of the samples used, as well as the construction of LS-SVM normal, robust and sparse regression are given. For obtain a robust LS-SVM regression was used the methods pseudo-observation and weighing. For obtain a sparse LS-SVM regression, different sampling splitting approaches have been used. Such as: sample partitioning by D-optimal experiment planning and various sample partitioning algorithms based on criterion of model selection. The algorithms for the partitioning of the sample based on the criterion of model selection include algorithms: replacements, rejections, points inclusions, Add/Del, Del/Add, and virtual support vectors. Otherwise, these algorithms can be called the algorithm of additional optimization of the composition of points. To verify the efficiency of the algorithms the computational experiment was conducted. An improvement of the obtained results accuracy with LS-SVM method was achieved by selecting the scale of the Gaussian kernel function. The parameters of the kernel function was selected by the value of criteria for assessing model quality. A fixed value of 10 is used as a regularization parameter. The final solution accuracy was tested by the value of determination criteria. In the table form are shown the results of the determination criterion for each method of obtaining the regression model. The results of the LS-SVM regressions are shown graphical form. These results showed that the use of the LS-SVM method is quite acceptable for solving applied problems.