Support vector machine method based on chaos and grey wolf optimization

A technology of support vector machine and gray wolf, which is applied in the field of computer science and can solve problems such as poor generalization performance of SVM

Active Publication Date: 2017-01-25
WENZHOU UNIVERSITY
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In practical applications, if their values ​​are too large or to

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  • Support vector machine method based on chaos and grey wolf optimization
  • Support vector machine method based on chaos and grey wolf optimization
  • Support vector machine method based on chaos and grey wolf optimization

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[0055] Such as figure 1 As shown, it shows the intelligent classification and prediction method based on chaotic gray wolf optimization algorithm and support vector machine of the present invention, the method adopts the chaotic gray wolf algorithm to optimize the key parameters of support vector machine including penalty coefficient C and kernel width γ, Construct the optimal support vector machine model based on the obtained optimal parameter values, and realize the classification and prediction of specific domain problems.

[0056] Such as figure 1 As shown, the method includes the following specific steps:

[0057] Step 1: Collect data related to the research question; for the research questions in different fields, the sample data format usually includes attribute indicators and category labels in the field. For example, when studying the identification of foreign fibers in cotton, the collection of its data set describes the foreign fibers from the three perspectives o...

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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...

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Application Information

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IPC IPC(8): G06K9/62G06N3/00
CPCG06N3/006G06F18/2411
Inventor 陈慧灵王名镜赵学华李强沈立明王科杰蔡振闹童长飞
Owner WENZHOU UNIVERSITY
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