Machine learning model training method and device and computer readable storage medium

Abstract

The invention relates to a machine learning model training method and device, a computer readable storage medium and computer equipment. The method comprises the steps of obtaining source domain sample data and target domain sample data of a to-be-trained machine learning model; determining a distribution density ratio between the target domain sample data and the source domain sample data; determining a source domain sample weight corresponding to each piece of source domain sample data according to the distribution density ratio; obtaining training sample data according to the source domainsample data and the target domain sample data, and determining training sample weights respectively corresponding to the training sample data according to the target domain sample weights respectivelycorresponding to the source domain sample weight and the target domain sample data; training the training sample data according to the weight of the training sample, and obtaining a trained machine learning model when a training ending condition is met. According to the scheme provided by the invention, the performance of the machine learning model obtained by training can be improved.

Description

technical field [0001] The present application relates to the field of computer technology, in particular to a machine learning model training method, device, computer-readable storage medium and computer equipment. Background technique [0002] Transfer learning refers to a learning process that uses the similarity between data, tasks or models to apply the model learned in the old domain (ie, the source domain) to a new domain (ie, the target domain). Simply understand that similarity is the basis of migration. The two scenarios where migration occurs need to have certain similarities, but there are differences at the same time. As a result, the model trained with source domain data cannot be directly used for target domain prediction. Through migration learning, the model parameters that have been learned in the source domain (also understood as the knowledge learned by the model) can be shared with the new model of the migration target domain in a certain way. It does no...

AI Technical Summary

Problems solved by technology

[0004] Based on this, it is necessary to provide a machine learning model training method, device, computer-readable storage medium and computer equipment for the technical problem that transfer learning is prone to negative transfer and affects the performance of the transferred model.

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