Method for determining a transfer learning boundary of heterogeneous relation data in public opinion data role recognition

Abstract

The invention discloses a method for determining a transfer learning boundary of heterogeneous relation data in public opinion data role recognition, and relates to the technical field of transfer learning. The problem that in the prior art, data in two fields are not combined for learning and then applied to a target domain, and the classification effect is inaccurate is solved. The method comprises the following steps: defining divergence for measuring the difference between two heterogeneous domains according to the formula (1), and defining the divergence for measuring the difference between the two heterogeneous domains, solving Empirical distances from two fields of the same abstract hypothesis class A by using the method, and giving an algorithm for converting the two classes into the same feature space; giving A difference boundary between an empirical distance and a real distance , a boundary for minimizing an error of a target domain, a giving generalization error which is strongest in generalization ability and combines training data of a source domain and the target domain, and obtaining a boundary of the error of the target domain by minimizing a joint error. And obtaining the obtained boundary ensures that a reasonable boundary value under the condition that the labeled data of the target domain is very few. The method is suitable for various identification problems in public big data and new media data platforms.

Description

technical field

[0001] The invention relates to a method for determining the boundary of transfer learning, and relates to the technical field of transfer learning. Background technique

[0002] In the process of transfer learning, if it is assumed that the source domain and the target domain have different distributions, feature spaces, and output spaces, it is necessary to consider whether the classification function of the source domain is applicable to the target domain. This leads to the domain adaptation problem, which can only be solved For this problem, the error between the source domain and the target domain can be obtained to give the transfer learning boundaries between different fields. The existing work provides a feasible idea for the research on the boundary of transfer learning and lays a certain foundation, but there are obviously some problems.

[0003] First, the training set and test set come from the same domain or have the same distribution. The prem...

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