Multi-dimensional training method and device of support vector machine

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...

AI Technical Summary

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 trained through the data relationship input by the user, it is difficult to mine the differences between the data. Dimensional relationship or connection, the accuracy of analysis after training is low, which does not meet the actual use requirements

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