Model training method, device, and computer device

Abstract

A model training method, apparatus, and computer device are disclosed. The method includes: determining a common feature space of the source domain sample set and the target domain sample set; determining a positive correlation feature term and a negative correlation feature term in the common feature space according to the tag value of the determined tag sample in the source domain sample set andthe eigenvalue of the determined tag sample in the common feature space; predicting a label value of an uncertain label sample in the target domain sample set according to the positive correlation feature item and the negative correlation feature item; uncertain label samples with predicted label values are integrated with the source domain sample set, and a classification model is obtained by training the integrated sample set.

Description

technical field [0001] The embodiments of this specification relate to the technical field of data processing, and in particular to a model training method, device, and computer equipment. Background technique [0002] In tasks such as machine learning, deep learning, and data mining, a large number of data samples with reliable labels can be used for training, and various forms of data models can be obtained to achieve business functions, such as preventing fraud risks and credit risks. [0003] With the development of business internationalization, it is generally hoped that the business functions implemented for domestic business scenarios can also be quickly applied to similar overseas business scenarios. However, although overseas business scenarios have similarities with domestic business scenarios, there are still differences. Unique characteristics, so if the data model trained based on domestic business scenarios is directly applied to overseas business scenarios, t...

AI Technical Summary

Problems solved by technology

[0003] With the development of business internationalization, it is generally hoped that the business functions implemented for domestic business scenarios can also be quickly applied to similar overseas business scenarios. However, although overseas business scenarios have similarities with domestic business scenarios, there are still differences. Unique characteristics, so if the data model trained based on domestic business scenarios is directly applied to overseas business scenarios, the accuracy of model recognition results will be low; at the same time, since the data samples with labels take a certain amount of time Therefore, if a new data model suitable for overseas business scenarios is retrained from scratch, it will take a long time and the training efficiency is low

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