A self-paced-cooperative training and learning method for character relabeling

Abstract

A self-paced-cooperative training-learning approach. First, obtain data from two fields of view of the target problem, including a small amount of labeled data and a large amount of unlabeled data, and initialize the model; establish corresponding optimization goals on the two fields of view; embed the self- step regularization term to achieve robust learning in this field of view; associate two fields of view through a regularization term; obtain a multi-view semi-supervised self-paced-cooperative training model with embedded robust learning mechanism and model interpretation; apply a small number of fields in the target field Labeled data and a large amount of unlabeled data and a semi-supervised multi-view learning model can obtain high-quality annotations of unlabeled data, and at the same time, a reliable learner under two views can be obtained. The invention aims to provide a robust learning model with a "replacement" mode for the traditional cooperative training algorithm, so that the data lacking labels in the target field can be labeled more accurately and with high quality.

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 of 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 labeled data available. 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 unlabele...

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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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