Incremental learning for transductive support vector machine

作者：

Highlights：

• We study the fast training method of TSVM under large-scale data.

• We propose an incremental learning algorithm for TSVM (ILTSVM) based on the path following technique under the framework of infinitesimal annealing.

• We also analyze the time complexity and convergence of ILTSVM.

• The experimental results show that compared with other incremental or batch learning algorithms, our algorithm is the most effective and fastest method for training TSVM.

摘要

•We study the fast training method of TSVM under large-scale data.•We propose an incremental learning algorithm for TSVM (ILTSVM) based on the path following technique under the framework of infinitesimal annealing.•We also analyze the time complexity and convergence of ILTSVM.•The experimental results show that compared with other incremental or batch learning algorithms, our algorithm is the most effective and fastest method for training TSVM.

论文关键词：Transductive support vector machine,Incremental learning,Non-convex optimization,Infinitesimal annealing

论文评审过程：Received 10 May 2021, Revised 6 June 2022, Accepted 13 August 2022, Available online 20 August 2022, Version of Record 24 August 2022.

论文官网地址：https://doi.org/10.1016/j.patcog.2022.108982