Construction method of domain self-adaptive classifier, construction device for domain self-adaptive classifier, data classification method and data classification device

Abstract

The invention provides a construction method of a domain self-adaptive classifier, a construction device for the domain self-adaptive classifier, a data classification method and a data classification device, wherein the construction method comprises the following steps that: a combined penalty objective function for constructing the domain self-adaptive classifier is determined, wherein the domain self-adaptive classifier is a classifier for classifying the data of a target domain and a source domain; the domain self-adaptive generalization error upper limit is determined on the basis of the combined penalty objective function; and on the basis of the domain self-adaptive generalization error upper limit, more than two classifiers are subjected to coordination training, and the domain self-adaptive classifier is constructed. The problem of distribution unconsistency of a source domain and a target domain in the prior art is solved; the more accurate classification can be realized on the premise of ensuring the convergence; the computation complexity is greatly reduced; and the problem of cross-domain information processing which cannot be handled by ordinary mode identification is solved.

Description

technical field [0001] The present invention relates to the technical field of data classification, in particular to a construction of a Domain Adaptation (Domain Adaptation, DA) classifier and a method and device for data classification. Background technique [0002] The core issues of artificial intelligence and machine learning are: how to represent the knowledge existing in the field, and how to use the existing knowledge for analysis and processing to obtain the knowledge that people are interested in. There is a key problem in the current field of machine learning research, that is, it is usually assumed that the training samples and test samples come from the same probability distribution, and the corresponding model and discriminant criteria are obtained by learning the training samples, and the output of the samples to be tested Make predictions. However, in practical applications, the distribution of training data and test data may be different, which makes the mo...

AI Technical Summary

Problems solved by technology

[0004] The embodiment of the present invention provides a method for constructing a domain adaptive classifier to achieve the purpose of reducing computational complexity and solving the problem of cross-domain information processing that cannot be handled by ordinary pattern recognition

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