Selection method for marking strategy, and related device

Abstract

The invention discloses a selection method for a marking strategy, and the method comprises the steps: carrying out the simulated marking processing and supervised learning training of a marked data set through employing a plurality of marking strategies, carrying out the training of a regression prediction model according to the index data in the process, and obtaining corresponding regression prediction models; carrying out the prediction calculation according to the index data through all regression prediction models when the index data of a marking task is obtained, and obtaining a prediction result; determining the marking strategy with the minimum manpower cost as the marking strategy to be used according to a prediction result and an expected result. The index data in the simulatedmarking processing is obtained and trained for obtaining the regression prediction models, the method can achieve the prediction of the marking strategy, and the marking strategy with the minimum manpower cost is determined according to a prediction result, thereby reducing the manpower cost while guaranteeing the training effect. The invention also discloses a selection device for the marking strategy, a server, and a computer readable storage medium, which all have the above beneficial effects.

Description

technical field [0001] The present application relates to the field of computer technology, and in particular to a method for selecting a labeling strategy, a selection device, a server, and a computer-readable storage medium. Background technique [0002] With the development of information technology, machine learning technology has been applied to more and more fields to improve the efficiency of dealing with problems in different application scenarios. Machine learning is mainly to train through a large amount of data to obtain an accurate recognition model. At the same time, it is necessary to continuously use the original data to test the recognition model to judge whether the recognition model meets the learning requirements. [0003] Among them, machine learning technology mainly has two learning methods, supervised learning and unsupervised learning. Supervised learning requires continuous human intervention during the learning process to adjust the state of the tr...

AI Technical Summary

Problems solved by technology

At this time, investing a lot of labor costs does not improve the overall effect of model training, resulting in some labeling work not playing a practical role, resulting in waste of human resources and unnecessary cost investment

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