Multi-dimensional training method and device of support vector machine

A technology of support vector machine and training method, which is applied in the field of multi-dimensional training method and device of support vector machine, can solve problems such as difficult to mine data relationship or connection, low analysis accuracy, complex operation, etc., and achieve improved classification and analysis Ability to improve the effect of linear separability

Pending Publication Date: 2022-03-15
GUANGDONG POWER GRID CO LTD +1
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Problems solved by technology

[0004] However, the current commonly used training methods have the following technical problems: because the training data requires the user to manually adjust the relationship between each data, the operation is complicated, and the SVM can only be

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  • Multi-dimensional training method and device of support vector machine
  • Multi-dimensional training method and device of support vector machine
  • Multi-dimensional training method and device of support vector machine

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[0073]Next, the technical solutions in the embodiments of the present invention will be described in connection with the drawings of the embodiments of the present invention, and it is understood that the described embodiments are merely the embodiments of the present invention, not all of the embodiments. Based on the embodiments in the present invention, all other embodiments obtained without creative labor are not made in the premise of creative labor.

[0074] The currently common training methods have the following technical problems: Since the data of the training requires the user to manually adjust the relationship between the data, the operation is complex, and the SVM can only train the data relationship input by the user, it is difficult to excavate data between different dimensions. Relationship or contact, the accuracy of analysis after training is low, does not meet the actual needs.

[0075] In order to solve the above problems, a multi-dimensional training method o...

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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.

Description

technical field [0001] The invention relates to the technical field of model training, in particular to a multi-dimensional training method and device for a support vector machine. Background technique [0002] Support vector machines (SVM for short) is a binary classification model, and its basic model is a linear classifier with the largest interval defined in the feature space. It is widely used in data mining, and its theoretical basis is the VC dimension theory and the principle of minimizing structure direction in statistics. The core of support vector machine is to find out an optimal separating hyperplane as the basis of classification. [0003] Since the standard support vector machine can only deal with linear situations, and most of the data that needs to be processed or analyzed now is nonlinear, in order to improve the classification ability of SVM, it needs to be trained on sub-linear data. At present, the commonly used training method is to input multiple no...

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Application Information

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IPC IPC(8): G06K9/62G06N20/10
CPCG06N20/10G06F18/214
Inventor 程晨陈扬范颖徐思尧李妍石振宇彭明洋张子媖杨强周刚
Owner GUANGDONG POWER GRID CO LTD
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