Machine learning method, device, equipment and system

Abstract

The invention discloses a machine learning method, device, equipment and system. The system comprises a processing module which is used for circularly executing a target operation until a test resultassociated with data in a test data set satisfies a preset condition. The processing module comprises a labeling module, a training module and a computing module. The storage module is used for storing the acquired data set and the test data set; The labeling module is used for labeling the data if the data in the data set is not labeled. The training module is used for carrying out feature extraction on the labeled data which is greater than or equal to a first preset threshold value, and inputting the extracted features into a pre-training model to obtain a trained training model; And the calculation module is used for inputting the data in the test data set into the trained training model, obtaining a test result associated with the data in the test data set, and assessing the test result to assess whether the test result meets a preset condition or not. By adopting the method, the model training speed can be improved.

Description

technical field [0001] The present application relates to the technical field of machine learning, and in particular to a machine learning method, device, device and system. Background technique [0002] With the growth of data scale, computing power, and storage capacity, artificial intelligence has developed rapidly and is widely used in various fields to mine the value of data. Behind the rapid development is a large amount of manpower investment. The industry's "artificial intelligence has as much intelligence as there are artificial intelligence" refers to the need to invest a lot of manpower before artificial intelligence technology can be combined with specific businesses. First, large-scale After the labeling, algorithm engineers need to spend a lot of time writing codes for training and parameter adjustment, and finally output a model. A complete machine learning process includes dataset labeling, dataset processing, feature processing, model training, model evalua...

AI Technical Summary

Problems solved by technology

[0003] However, in the actual production environment, a model that can be commercialized (mainly unsupervised learning) often requires multiple rounds of training iterations, and the dataset that needs to be collected and labeled to improve the accuracy of the model is a closed-loop process. Each iteration starts from importing unlabeled raw data into the labeling system, then exporting from the lab

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