Incremental learning-fused support vector machine multi-class classification method

Abstract

The invention relates to an incremental learning-fused support vector machine multi-class classification method, and aims to reduce sample training time and improve classification precision and anti-interference performance of a classifier. The technical scheme comprises the following steps of: 1, extracting partial samples from total samples at random to serve as a training sample set D, and using the other part of samples as a testing sample set T; 2, pre-extracting support vectors from the training sample set D; 3, performing support vector machine training on a pre-extracted training sample set PTS by using a cyclic iterative method so as to obtain a multi-class classification model M-SVM; 4, performing binary tree processing on the multi-class classification model M-SVM to obtain a support vector machine multi-class classification model BTMSVM0; 5, performing incremental learning training on the multi-class classification model BTMSVM0 to obtain a model BTMSVM1; and 6, inputting the testing sample set T in the step 1 into the multi-class classification model BTMSVM1 for classification. The incremental learning-fused support vector machine multi-class classification method is used for performing high-efficiency multi-class classification on massive information through incremental learning.

Description

Technical field [0001] The invention relates to the technical field of intelligent information processing and machine learning, in particular to a support vector machine multi-class classification method incorporating incremental learning. It is suitable for efficient multi-class classification of massive information through incremental learning under complex attribute conditions. Background technique [0002] Support Vector Machine (Support Vector Machine) is a machine learning method developed in recent years. It is constructed based on the principle of structural risk minimization. It has strong learning ability and generalization performance, and can solve small samples, High dimensionality, nonlinearity, local minima and other problems are widely used in pattern classification and nonlinear regression. The traditional support vector machine is proposed for the two-class classification problem and cannot be directly used for multi-class classification, but in practical appli...

AI Technical Summary

Problems solved by technology

Ying w et al. (2006) proposed a binary tree-based support vector machine multi-classification algorithm (BTSVM for short) on the basis of overcoming the shortcomings of the above algorithms, which has better classification effect and classification efficiency, but the algorithm's anti-interference ability is poor , has high requirements for training data, which hinders its application to a certain extent.

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