Transfer learning method based on semi-supervised clustering

Abstract

The invention provides a transfer learning method based on semi-supervised clustering. The transfer learning method based on the semi-supervised clustering comprises the following steps: calculating similarity and average similarity of data in each class of target data and auxiliary data; according to the average similarity, obtaining a similarity weight vector of the target data and a class tag; taking the vector with the maximum weight as a tag of the target data; with the target data as a centroid, performing K-means clustering into clusters, wherein the tag, having the maximum proportion of data in each cluster to the total data of the class to which the cluster belongs, is taken as a cluster tag; comparing a classification result with a pre-classification result; and in the finally-formed similarity weight vector of the target data, selecting a data tag with the maximum weight as the data tag of the target data so as to form a final classifier. The invention provides the transfer learning method based on the semi-supervised clustering, which can transfer a classifying method and a classifying technology from one field to another field and can improve the precision of the classification result.

Description

technical field [0001] The invention relates to a method in the field of machine learning, in particular to a migration learning method based on semi-supervised clustering. Background technique [0002] The traditional classifier in machine learning requires that the source data and the target data must have the same distribution. However, the information development in modern society is changing with each passing day. This assumption is difficult to realize in real life. When data with different distributions appears, traditional classifiers have to collect a large amount of data again, and experts will analyze and label these data. A lot of manpower and time will be invested, which is not only inefficient, but also expensive. Transfer learning can overcome the shortcomings of collecting data from scratch. It can transfer its useful methods and techniques from different but similar fields to the target field, and help the target field data to be classified. [0003] Ther...

AI Technical Summary

Problems solved by technology

For new data, only the bridge refinement process can be performed again, which is very inefficient for the entire migration learning process

In addition, the current research on transfer learning methods is facing a common problem: the accuracy is not high

However, due to the limitations of knowledge and ability, the classification accuracy of the final classifier is not high, that is, the classification error is relatively large.

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