Support vector machine method based on chaos and grey wolf optimization

Abstract

The invention provides a support vector machine method based on chaos and grey wolf optimization, specifically,the grey wolf algorithm chaotization is combined with the support vector machine; two key parameters of penalty coefficient C and kernel width Upsilon of the support vector machine are optimized by the grey wolf chaotization algorithm with outstanding whole searching ability; the best extreme parameter value of the learning machine is obtained so that the application can obtain accurate and intelligent decision effect; the machine can help the decision mechanism effectively to make scientific decision, so the machine has important application value.

Description

technical field [0001] The invention relates to a support vector machine method based on chaotic gray wolf optimization, which belongs to the field of computer science. Background technique [0002] Grid search and gradient descent are currently the two most commonly used parameter optimization methods for support vector machines (SVM). Grid search is an exhaustive search method. It generally divides the specified parameter space by setting reasonable interval upper and lower limits and interval steps, and then trains and predicts the parameter combinations represented by each grid node. These A group of parameters with the highest values ​​in the prediction results are used as the best parameters of the final SVM model. Although this exhaustive search method can guarantee the optimal parameter combination in a given parameter space to a certain extent, as the parameter space increases, its search efficiency will be greatly reduced, especially when setting reasonable interv...

AI Technical Summary

Problems solved by technology

In practical applications, if their values ​​are too large or too small, the generalization performance of SVM will deteriorate.

Images