Training method of SVM (Support Vector Machine) classifier based on semi-supervised learning

Abstract

The invention especially discloses a training method of an SVM (Support Vector Machine) classifier based on semi-supervised learning. The training method comprises the following steps of: step 1, training an initial SVM classifier through an initial labelled sample set; step 2, looking for samples with high classifying confidence degrees from an unlabelled sample set U to constitute a sample set S with high confidence degree; step 3, judging an amount of information of each sample in the sample set S with high confidence degrees according to a method described in the graph 3, removing the samples from the sample set S with high confidence degrees if the amount of information is large , and placing the samples back into the unlabelled sample set U; step 4, adding the samples with high confidence degrees and large amount of information after the samples are automatically labeled by a machine in the sample set S into a labeled sample set L of the SVM classifier; step 5, using the renewed labeled sample set L to retrain the SVM classifier; and step 6, judging whether the SVM classifier either exists a loop or continuously iterates according to a stopping criterion.

Description

technical field [0001] The invention relates to the field of machine learning, in particular to a training method for a support vector machine classifier based on semi-supervised learning. Background technique [0002] In the field of machine learning, in order to train a classifier with good classification performance, it is necessary to use a large number of labeled samples to participate in the training. However, the labeling of samples is tedious and requires a lot of time and energy, which makes it expensive to obtain labeled samples through manual labeling. To overcome this difficulty, experts have proposed semi-supervised learning techniques. Semi-supervised learning is a cyclic and iterative process, which can be divided into the following categories: self-training semi-supervised learning, semi-supervised learning with generative models as classifiers, semi-supervised learning with transductive support vector machines, and semi-supervised learning based on graphs. ...

AI Technical Summary

Problems solved by technology

However, it is not enough just to have a high classification confidence. We hope that the samples have a large information content while ensuring a high confidence.

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