Domain adaptive support vector machine generation method

Abstract

The invention discloses a domain adaptive support vector machine generation method, belongs to the field of domain adaptive learning, and solves the technical problem of how to fully mine generality information of data between domains in domain adaptive learning and overcome dependency on tags of source domain data and target domain data. The method comprises the following steps of generating synthetic data through an over-sampling algorithm based on the source domain data and the target domain data; based on the synthetic data and the target domain data, mining a shared implicit characteristic space between the synthetic data and the target domain data according to a probability distribution integral mean square error minimum principle; and based on the target domain data, performing training on an expanded characteristic space consisting of the shared implicit characteristic space and an original characteristic space to generate a domain adaptive support vector machine. The generality information of the source domain data and the target domain data can be fully mined. According to the method, the generality information of the source domain data and the target domain data can be mined without depending on the tags of the source domain data and the target domain data.

Description

Technical field [0001] The present invention relates to the field of field adaptation learning, in particular to a method for generating field adaptation support vector machines. Background technique [0002] A basic assumption of traditional machine learning is that the distribution of training data and test data are the same, and its learning method has achieved great success in the field of traditional pattern recognition. At present, there are some new applications in the field of machine learning. The amount of training data with the same distribution as the test data is very small. The use of traditional machine learning methods is not enough to train a reliable learning model, but there may be a large number of closely distributed in related fields Training data is very important in how to effectively use a large amount of data and information in related fields in a new field. This is the problem to be explored in field adaptation learning. Domain adaptive learning is a n...

AI Technical Summary

Problems solved by technology

[0005] The technical task of the present invention is to address the above deficiencies and provide a method for generating domain-adaptive support vector machines to solve how to fully tap the commonality of data between domains and overcome the dependence on source domain data and target domain data labels in domain adaptation learning The problem

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