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A multi-label learning and multi-label technology, applied in the field of multi-label learning, achieves the effect of overcoming excessive feature dimension, strong generalization ability, and simple training
Inactive Publication Date: 2018-08-03
INST OF COMPUTING TECH CHINESE ACAD OF SCI
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[0008] The purpose of the present invention is to overcome the defects of the above-mentioned prior art and provide an improved multi-label learning method to improve the effect of multi-label learning when the labeled samples are insufficient and the feature dimension of the original data is too high
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[0049] In order to make the purpose, technical solution, design method and advantages of the present invention clearer, the present invention will be further described in detail through specific embodiments in conjunction with the accompanying drawings. It should be understood that the specific embodiments described here are only used to explain the present invention, not to limit the present invention.
[0050] According to an embodiment of the present invention, a multi-label learning method is provided. In short, the method includes a process of training a multi-label learning model and a process of using the trained model to predict a label set of a new instance.
[0051] see figure 1 As shown, the training process of the multi-label learning model of an embodiment of the present invention includes the following steps:
[0052] Step S110, initialize a multi-label learning model including an autoencoder and a multi-classification model.
[0053] The multi-label learning m...
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Abstract
The invention provides a training method for a multi-mark learning model. The method comprises the steps that a given multi-mark data training set is used to train the multi-mark learning model whichcomprises an automatic encoder and a multi-classification model; the loss function of the multi-mark learning model is examined, and the loss function of the multi-mark learning model comprises a lossterm of the automatic encoder and a loss term of the multi-classification model; if the loss function does not satisfy a predetermined accuracy requirement, the relevant parameters of the automatic encoder and the multi-classification model are iterated; and a training classifier corresponding to the multi-classification model and the optimization weight matrix and offset vector of the automaticencoder are acquired. According to the invention, an acquired training model multi-classifies data; a large amount of unmarked data can be effectively utilized; and the classification accuracy is improved.
Description
technical field [0001] The invention relates to the technical field of machine learning, in particular to a multi-label learning method. Background technique [0002] Multi-label learning refers to attaching multiple different class labels to data instances. For example, when classifying a piece of news, it may cover multiple topics such as economy and security; another example is to label a video, the content of the video may be Relevant to themes like sci-fi, comedy, action, etc. Specifically, suppose X=R d Represents the d-dimensional input data instance space, Y={y 1 ,y 2 ,...,y q} represents a label space with q labels, and the task of multi-label learning is to learn from multi-label data D={(x i ,Y i )|1≤i≤m} to learn the function h:X→Y. where, for each multilabel sample (x i ,Y i ), x i ∈X,x i is a d-dimensional vector (x i1 ,x i2 ,...,x id ) T , Y i is instance x i Has a collection of tags. After obtaining the multi-label function h, for a new in...
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