Classification model training method and device, classification method and device, medium and equipment

Abstract

The invention discloses a classification model training method and device, a classification method and device, a medium and equipment. The classification model training method comprises the steps of obtaining first sample data which is not provided with a classification label, inputting the first sample data into a pre-trained basic classification model, and determining the probability that the first sample data is provided with a preset classification label, wherein the basic classification model is obtained through training based on second sample data provided with a preset classification label; determining the weight of the first sample data based on the probability of setting a classification label by the first sample data; and training a to-be-trained classification model based on second sample data, the first sample data and the weight of the first sample data to obtain a target classification model. The label processing of the sample data without a label is realized, manual label setting of a sample is replaced, the time and labor cost of a sample data preprocessing process are reduced, and the weak supervision training of the classification model is further realized.

Description

technical field [0001] The embodiments of the present invention relate to the technical field of deep learning, and in particular, to a training method, classification method, device, medium, and equipment for a classification model. Background technique [0002] The training method of the machine learning model based on the supervision method is a common training method of the machine learning model, wherein, the training method requires a sufficient number of labeled samples. [0003] In the process of realizing the present invention, the inventors have found that there are at least the following technical problems in the prior art: in many practical application scenarios, it takes a lot of manpower and material resources to obtain the marks of samples, which is expensive, resulting in It is often a small number of samples with classification labels and a large number of samples without classification labels, which cannot realize the above-mentioned supervised training pro...

AI Technical Summary

Problems solved by technology

[0003] In the process of realizing the present invention, the inventors have found that there are at least the following technical problems in the prior art: in many practical application scenarios, it takes a lot of manpower and material resources to obtain the marks of samples, which is expensive, resulting in It is often a small number of classified label samples and a large number of unclassified labeled samples, which cannot realize the above-mentioned supervised training process

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