›› 2013, Vol. 26 ›› Issue (9): 10-.

• 论文 • 上一篇    下一篇

粒子群算法支持向量机的半监督回归

马蕾   

  1. (西北工业大学明德学院 计算机信息技术系,陕西 西安 710124)
  • 出版日期:2013-09-15 发布日期:2013-09-25
  • 作者简介:马蕾(1985—),女,硕士,助教。研究方向:智能信息处理。E-mail:357023814@qq.com

Semi-Supervised Regression Based on Particle Swarm Optimization and Support Vector Machine

MA Lei   

  1. (School of Computer Information and Technology,Mingde College of Northwestern Polytechnical University,Xi'an 710124,China)
  • Online:2013-09-15 Published:2013-09-25

摘要:

将基于粒子群算法的支持向量机与半监督学习理论相结合,提出了粒子群算法支持向量机的半监督回归模型。针对典型的实验数据集进行实验,并将实验结果与常规的遗传算法支持向量机和粒子群支持向量机模型进行对比。实验结果表明,粒子群算法支持半监督回归模型明显提高了回归估计的精度。

关键词: 半监督学习, 支持向量机, 粒子群算法, 遗传算法

Abstract:

The paper combines support vector machine based on particle swarm optimization with semi-supervised regression theory to bring about a semi-supervised regression model.The paper uses the experimental datasets to compare with the genetic algorithm supervised support vector machine model and the particle swarm optimization support vector machine.Experimental results show that the semi-supervised regression model based on particle swarm optimization support vector machine can improve the precision of regression estimates.

Key words: semi-supervised learning;support vector machine;particle swarm optimization;genetic algorithm

中图分类号: 

  • TP181