Dynamic machine learning modeling method based on sample recommending and labeling

Abstract

The invention relates to a dynamic machine learning modeling method based on sample recommending and labeling and belongs to the technical field of machine learning. According to the method, a total sample set is clustered according to a CURE algorithm, the center sample and the representative sample of each cluster in results are recommended and labeled, and then, a model is established. The method has the beneficial effects that data sets of unknown categories are recommended and labeled when supervised learning problems are processed, and thus, the processing is effective, timesaving and laborsaving; and wrong samples are repeatedly trained by using machine learning, so that the model can more accurately judge the category of new data sets.

Description

technical field [0001] The invention belongs to the technical field of machine learning, in particular to a dynamic machine learning modeling method based on sample recommendation and labeling. Background technique [0002] With the continuous development of science and technology, scholars have gradually begun to study how to make intelligent machines (computers) replace humans to complete some complex intellectual labor, thereby liberating human labor. As one of the core contents of artificial intelligence, machine learning technology understands the human learning process and understanding process from the perspectives of physiology and cognition, so as to simulate the establishment of learning models or understanding models. And abstract from this process into various learning methods and theories. In short, machine learning technology aims to obtain similar learning or understanding capabilities by allowing intelligent machines (computers) to simulate the learning proc...

AI Technical Summary

Problems solved by technology

[0005] However, for traditional machine learning models, refer to related articles such as "Long Query User Satisfaction Analysis Based on User Behavior", which has many defects. Influence; the traditional learning model is trained based on a batch of data at one time, which must have the characteristics of timeliness; the traditional learning model treats each sample equally, does not pay attention to the samples that have been wrongly judged, and improves the results, etc.

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