Self-paced-cooperative training learning method

Abstract

The invention discloses a self-paced-cooperative training learning method. The method comprises the following steps of: obtaining data, from two visual fields, of a target problem, and initializing a model, wherein the data comprises a small amount of labeled data and a large amount of unlabeled data; respectively determining corresponding optimization targets on the two visual fields; embedding a self-paced regular term in a loss function of each visual field so as to realize steady learning under the visual field; associating the two visual fields through a regular term; obtaining a multi-visual field semi-supervised self-paced-cooperative training model which is embedded into a steady learning mechanism and has model interpretation; and obtaining high-quality labeling of the unlabeled data by applying the small amount of labeled data and large amount of unlabeled data in a target field and a semi-supervised multi-visual point learning model, and obtaining reliable learning devices under the two visual fields at the same time. The invention aims at providing a steady learning model with a replacement mode for the traditional cooperative training algorithms to ensure that data lack of labeling in the target field can obtain more correct and high-quality labeling.

Description

technical field [0001] The invention relates to a multi-view semi-supervised learning model and method, in particular to a novel self-paced-cooperative training model and learning method. Background technique [0002] There are a lot of real-time data on the Internet, such as news, pictures, videos, etc., but most of these data only have vague descriptions about events, and some even have no labeled information at all. When we want to perform query or classification tasks, in traditional machine learning algorithms, this part of unlabeled information or weakly labeled data is basically not used, resulting in a large loss of available information. This type of data is characterized by a large amount of unlabeled data and limited available labeled data. Therefore, how to mine information in unlabeled data has become a technology that has emerged in the field of machine learning in recent years. On the premise of making full use of labeled data, extract information from unlab...

AI Technical Summary

Problems solved by technology

Furthermore, it is very important for a machine learning method to have a machine learning optimization model that can explain its essence, which is also one of the basic three elements of machine learning (i.e., training data, decision function, performance measure or optimization objective) , while the traditional collaborative training methods basically lack a perfect model explanation

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