Support vector regression machine model selection method

Abstract

The invention discloses a support vector regression machine model selection method, and brings forward a novel support vector regression machine model selection method based on a hybrid kernel function and volume Kalman filtering, for solving the problem of model selection of a support vector regression machine. The hybrid kernel function is selected as a kernel function of the support vector machine, a combination coefficient of the hybrid kernel function is embedded into a superparametric state vector composed of a kernel function parameter and a regression parameter, accordingly, the problem of model selection is converted into a problem of state estimation of a nonlinear system, and then based on the high-performance volume Kalman filtering, superparametric estimation is carried out. A simulation experiment shows that the method brought forward by the invention, compared to a volume Kalman filtering support vector regression machine model selection method of a single kernel function and a genetic algorithm, has the following advantages: the generalization capability of a decision regression function obtained through the method is greater, and the prediction precision is higher.

Description

technical field [0001] The invention relates to a support vector machine related technology, in particular to a support vector regression machine model selection method. Background technique [0002] In 1995, Corinna Cortes and Vapnik first proposed Support Vector Machine (SVM), which is a general learning method based on statistical learning theory. Due to its powerful nonlinear processing ability and generalization ability, SVM shows many unique advantages in solving small sample, nonlinear and high-dimensional pattern recognition, and has successfully solved various nonlinear and non-separable Machine learning problems are therefore widely used in classification and regression problems. However, there are still some problems that need to be solved and improved in the support vector machine method, mainly as follows: support vector machine classification theory is proposed for two types of classification problems, however, the classification problems in the real world, su...

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Problems solved by technology

However, there are still some problems that need to be solved and improved in the support vector machine method, mainly as follows: support vector machine classification theory is proposed for two types of classification problems, however, the classification problems in the real world, such as ship recognition, font recognition, human Face recognition and so on belong to the category of multi-classification. Therefore, how to use support vector machines to solve multi-classification problems more effectively; support vector machines need to perform a large number of matrix operations in the secondary optimization process. In most cases, the optimization algorithm takes up Most of the algorithm time is spent, which makes the storage space and calculation time become the bottleneck of solving the quadratic programming problem. How to solve this bottleneck problem; the kernel function and kernel parameters play a key role in the classification and fitting ability of the support vector machine How to determine the kernel function and the optimal kernel parameters to ensure the effectiveness of the algorithm

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