Online semi-supervised classification method and system based on multi-view active learning
A classification method and active learning technology, applied in the field of machine learning, can solve the problems of complex screening process and low classification efficiency, achieve high-precision classification, save manpower, and improve classification efficiency
Active Publication Date: 2021-06-22
INST OF AUTOMATION CHINESE ACAD OF SCI
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However, active learning rules are designed for single-view data, and there is no pre-screening process, resulting in complicated screening process and low classification efficiency
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[0095] Preferred embodiments of the present invention are described below with reference to the accompanying drawings. Those skilled in the art should understand that these embodiments are only used to explain the technical principles of the present invention, and are not intended to limit the protection scope of the present invention.
[0096] The purpose of the present invention is to provide an online semi-supervised classification method based on multi-view active learning. By acquiring multi-view data online at any time and using the classification interval to update the classifier, the classification efficiency can be improved and manpower can be saved. High-precision classification when labeling few samples.
[0097] In order to make the above objects, features and advantages of the present invention more comprehensible, the present invention will be further described in detail below in conjunction with the accompanying drawings and specific embodiments.
[0098] Such ...
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The present invention relates to an online semi-supervised classification method and system based on multi-view active learning. The online semi-supervised classification method includes: step S1: obtaining multi-view data at the current moment; step S2: based on the multi-view data, through Multi-view prediction function, estimate the feature category label and the second largest label t of the perspective data to represent the current moment; Step S3: Determine the classification interval q according to the feature category label and the second largest label t ; Step S4: According to the classification interval q t , to update the classifier. The online semi-supervised classification method based on multi-view active learning in the present invention acquires multi-view data online at any time, and uses the classification interval to update the classifier, which can improve classification efficiency, save manpower, and realize high-precision classification when a small number of samples are marked .
Description
technical field [0001] The invention relates to the technical field of machine learning, in particular to an online semi-supervised classification method and system based on multi-view active learning. Background technique [0002] With the continuous development of information technology, more and more data are obtained from different information sources, spaces and modalities, and these data with different attributes constitute multi-view data sets. Compared with single-view learning, multi-view learning can explore useful features of each view to improve learning ability, so multi-view learning has received extensive attention. [0003] Online learning can effectively process real-time data streams and large-scale data, and is a research hotspot in the field of machine learning. Online learning can incrementally learn classification models from data streams without reusing previous samples, which is suitable for dynamic growth and large-scale data sets. [0004] In rece...
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IPC IPC(8): G06K9/62
CPCG06F18/2155
Inventor 聂祥丽黄夏渊贾立好乔红张波
Owner INST OF AUTOMATION CHINESE ACAD OF SCI