Multi-dimensional training method and device of support vector machine

By discretizing and kernel function mapping the training sample data of the support vector machine, and optimizing the objective function in combination with the gradient descent algorithm, the problems of complex operations and low classification accuracy in the existing technology are solved, and the classification and analysis capabilities of SVM are improved. .

CN114186620AInactive Publication Date: 2022-03-15GUANGDONG POWER GRID CO LTD +1
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Patent Information

Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
GUANGDONG POWER GRID CO LTD
Filing Date
2021-11-30
Publication Date
2022-03-15
Estimated Expiration
Not applicable · inactive patent

AI Technical Summary

Technical Problem

The existing support vector machine (SVM) training method requires users to manually adjust the relationship between data. The operation is complex and difficult to mine data relationships in different dimensions, resulting in low classification accuracy and unable to meet actual needs.

Method used

By discretizing the training sample data set, the classification contribution parameters of each attribute feature vector are calculated, and the kernel function is used to perform data mapping on these parameters, and the objective function is optimized with the gradient descent algorithm to train the support vector machine model.

Benefits of technology

The classification and analysis capabilities of the support vector machine are improved, the data gain weight is determined through kernel function mapping, and the linear separability of the mapped dimensional data is enhanced, thereby improving the classification accuracy.

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Abstract

The invention discloses a multi-dimensional training method and device for a support vector machine, electronic equipment and a computer readable storage medium, and the method comprises the steps: carrying out the discretization of a training sample data set, and obtaining a discretized data set, the discretized data set comprises a plurality of different attributes, and each attribute corresponds to a plurality of feature vectors; calculating a classification contribution parameter of each attribute feature vector to obtain a plurality of classification contribution parameters; performing data mapping on the plurality of classification contribution degrees by using a kernel function to obtain a target function; and optimizing and training the objective function by using a gradient descent algorithm to obtain a support vector machine model. According to the method, different dimension data are mapped through the kernel function, the data gain weight can be determined, the linear separable effect of the mapped dimension data can be improved, and then the classification and analysis capability of the SVM can be improved.
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Citation Information

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