计算机工程2009，Vol.35Issue(15)：187-189,3.
利用高斯域的半监督回归和主动学习
Semi-Supervised Regression and Active Learning with GF
崔鹏 1张汝波2
作者信息
- 1. 哈尔滨工程大学计算机学院,哈尔滨150001
- 2. 哈尔滨理工大学计算机学院,哈尔滨150080
- 折叠
摘要
Abstract
A Gaussian Fields(GF) on nearest neighbor graph is defined by using a non-parametric technique. On the basis of it, a MAP criterion which can automatically set model parameter and numbers of nearest-neighbor k is proposed and entropy maximization query selection method for active learning by using supervised and unsupervised information is specified. Experimental results demonstrate effectiveness of GF compared with semi-active learning method.关键词
高斯域/半监督回归/主动学习/熵/Cholesky分解Key words
Ganssian fields(GF)/ semi-supervised regression/ active learning/ entropy/ Cholesky decomposition分类
信息技术与安全科学引用本文复制引用
崔鹏,张汝波..利用高斯域的半监督回归和主动学习[J].计算机工程,2009,35(15):187-189,3.
