Classification model training method and device, equipment and medium

Abstract

The invention discloses a classification model training method and device, equipment and a medium. The classification model training method comprises the following steps: constructing a vertex feature matrix, an adjacent matrix and a vertex label matrix based on a graph data set, wherein the vertex label matrix comprises label information of each vertex of the graph data set; inputting the vertex feature matrix, the adjacent matrix and the vertex label matrix into a Teamer graph wavelet neural network in a classification model for supervised training, and determining corresponding supervised training loss in the training process; inputting the vertex feature matrix and the adjacent matrix into a Student graph wavelet neural network in a classification model for unsupervised training, and determining corresponding unsupervised training loss in the training process; determining a target training loss based on the supervised training loss and the unsupervised training loss; and when the target training loss converges, outputting the current classification model to obtain a trained classification model. Therefore, the classification accuracy of the classification model can be improved.

Description

technical field [0001] The present application relates to the technical field of classifiers, and in particular to a classification model training method, device, equipment and medium. Background technique [0002] With the rapid development of information technologies such as cloud computing, the Internet of Things, mobile communications, and smart terminals, new applications represented by social networks, communities, and blogs are widely used. These applications continue to generate a large amount of data, which is convenient for modeling and analysis with graphs. Among them, the vertices represent individuals or groups, and the connecting edges represent the connections between them; the vertices are usually attached with label information to represent the age, gender, location, hobbies and religious beliefs of the modeled objects, and many other possible feature. These characteristics reflect individual behavior preferences from various aspects. Ideally, each social ...

AI Technical Summary

Problems solved by technology

This is because, for the purpose of protecting personal privacy, more and more social network users are more cautious when sharing personal information, so social network media can only collect part of the user's information

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