An Incremental Learning Method Based on the Geometric Meaning of Support Vectors

Abstract

The invention discloses an incremental learning method based on the geometric meaning of support vectors, which includes the following steps: firstly, an effective method for selecting a given number of convex hull vertex samples is proposed, which is called the CHVS method, and it is proved theoretically that the The selected sample is the convex hull vertex sample; secondly, by using the CHVS method in each type of sample set, select those important samples from the large amount of data training samples that can maintain the information of various convex hulls to the greatest extent, which is called the VS method; Finally, these selected important samples are used to retrain with new samples to obtain an updated classifier. The invention realizes an effective SVM incremental learning classification method, which is a general method. Compared with other classic SVM incremental learning methods, the incremental learning method of the present invention is more suitable for online incremental learning tasks with a large amount of data , has a good application prospect.

Description

technical field [0001] The invention relates to the field of computer pattern recognition, in particular to an incremental learning method based on the geometric meaning of a support vector machine (SVM). Background technique [0002] Classifier design is the research focus in the field of computer pattern recognition, because classifiers are the basic tools of pattern recognition research. In general, the current classifier algorithm has two key problems: (1) the generalization ability of the classifier; (2) the time spent in training the classifier. [0003] The generalization ability of the classifier is the ability of the classifier to predict the unknown sample category, that is, the level of classification accuracy. Support vector machine, or SVM, is a machine learning method based on statistical learning theory. It is a concrete realization of the idea of ​​structural risk minimization and has good generalization ability. By introducing the kernel mapping method, it...

AI Technical Summary

Problems solved by technology

In general, the current classifier algorithm has two key issues: (1) the generalization ability of the classifier; (2) the time spent training the classifier

Although this type of method overcomes the defect that the previous type of method may lose important data samples during the incremental learning process and improves the classification accuracy, it requires many iterations to reach the stop condition when processing large-scale data, so it cannot meet the requirements of real-time online learning. requirements

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