Computer Science ›› 2015, Vol. 42 ›› Issue (6): 82-87.doi: 10.11896/j.issn.1002-137X.2015.06.019

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Three-way Decisions-based Incremental Learning Method for Support Vector Machine

XU Jiu-cheng, LIU Yang-yang, DU Li-na and SUN Lin   

  • Online:2018-11-14 Published:2018-11-14

Abstract: Aiming at the problems that the typical incremental learning algorithm for support vector machine (SVM) loses a lot of useful information and the objectivity that existing incremental learning algorithms for SVM aspire to classification accuracy merely,the subjectivity of loss functions of three-way decisions was introduced to incremental lear-ning algorithms for SVM,and a three-way decisions-based incremental learning method for SVM was proposed.Firstly the conditional probability of three-way decisions was denoted by the ratio of feature distances and center distances.Secondly the objects of boundary region of three-way decisions were regarded as boundary vectors to be trained with the original support vectors and the newly added samples.Finally,simulation experiments were done.The results show that the proposed method not only makes full use of the useful information to improve the classification accuracy,but also revises the objectivity of existing incremental learning algorithms for SVM to some extent.Besides,the computation problem of conditional probability of three-way decisions is resolved.

Key words: Three-way decisions,Support vector machine,Incremental learning,Conditional probability,Boundary vectors

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