Method, device and equipment for determining training sample

Abstract

The invention relates to the technical field of machine learning, and discloses a method for determining a training sample, and the method comprises the steps: obtaining an unlabeled sample set and a plurality of alternative models, and distributing corresponding labeled sample sets for the alternative models, wherein the plurality of alternative models are active learning models with different sample selection strategies; training corresponding alternative models by using the labeled sample set to obtain evaluation models corresponding to the alternative models; evaluating the unlabeled sample set by using an evaluation model to obtain a first evaluation result; and determining a training sample according to the first evaluation result. According to the method, the active learning models with different sample selection strategies are trained through the labeled sample set to obtain the corresponding evaluation models, and the unlabeled sample set is evaluated by using the evaluation models to determine the training samples, so that the tendency of a single active learning algorithm is avoided, and the diversity of the training samples is improved. The invention also discloses a device and equipment for determining the training sample.

Description

technical field [0001] The present application relates to the technical field of machine learning, for example, to a method, device and equipment for determining training samples. Background technique [0002] With the advent of the era of big data, data analysis tasks have become more difficult. Data with precise labeling information is especially rare due to the large size and low quality of the data. Therefore, how to determine the most valuable part of data from massive data for manual labeling, thereby reducing the cost of data labeling has become a difficult problem. [0003] In the process of implementing the embodiments of the present disclosure, it is found that there are at least the following problems in related technologies: the existing technology determines samples for a single active learning algorithm, and different active learning algorithms have different tendencies, which will lead to the final choice of The sample lacks diversity. Contents of the inve...

AI Technical Summary

Problems solved by technology

[0003] In the process of implementing the embodiments of the present disclosure, it is found that there are at least the following problems in related technologies: the existing technology determines samples for a single active learning algorithm, and different active learning algorithms have different tendencies, which will lead to the final choice of samples lack diversity

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