Semi-supervised classification method and system

Abstract

The invention discloses a semi-supervised classification method and system, and belongs to the technical field of computers, and the method comprises the steps: collecting original data, and converting the original data into a sample set; setting L = [1, 2-c-l] to represent a set of label values, c = 1, 2-l, establishing a state transition matrix m, wherein n = 1, 2-l, Tm, and n represents a transition probability from a state m to a state n; setting an annotation interval according to the minimum element on the diagonal line in the state transition matrix; labeling the samples according to the labeling interval to obtain a labeled sample set and an unlabeled sample set; and training the constructed support vector machine model by using the labeled sample set and the unlabeled sample set to obtain a trained support vector machine. The sample classification well reflects the actual situation of data classification, the model is trained by using the classified data, and the accuracy of semi-supervised classification is improved.

Description

technical field [0001] The invention relates to the field of computer technology, in particular to a semi-supervised classification method and system. Background technique [0002] How to utilize massive amounts of data is an important task facing current machine learning. The traditional support vector machine is a supervised learning method that requires a large number of labeled samples for training. However, in practical applications, since most of the sample data that can be used is unlabeled, and there are fewer labeled sample points, if only these few labeled samples are used, it will result in information that exists in a large number of location-labeled samples. was lost. Therefore, some scholars have proposed a method of semi-supervised learning, which is to use both unlabeled sample data knowledge and a small amount of labeled sample data knowledge in the semi-supervised learning process. However, existing semi-supervised learning methods do not fully exploit sp...

AI Technical Summary

Problems solved by technology

However, the existing semi-supervised learning methods do not fully exploit the spatial smoothness, resulting in a large number of misclassifications.

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