摘要
由于需要利用高斯函数逼近潜变量函数的后验概率,传统高斯过程分类算法通常都存在计算复杂度高的问题.对此,提出一种新高斯过程分类算法.该算法的基本思想为:首先,利用Parzen窗方法估计出每个训练样本的后验概率;然后,通过所得到的后验概率将原始分类问题变换为回归问题;进而分析地得到潜变量函数后验概率的显式表达式,以避免逼近后验概率所面临的高计算复杂度问题.仿真实验结果表明,所提出的算法在分类精度上优于已有的高斯过程分类算法.
Because the posterior probability of the latent function needs to be approximated by a tractable Gaussian function, the traditional Gaussian process classification algorithms usually suffer from high computational cost. Therefore, a new Gaussian process classification algorithm is proposed. The basic idea is to use Parzen-window method to estimate the posterior probability of training data, and then transform the classification problem to a regression problem based on the obtained posterior probability. As a result, the explicit expression of the posterior probability of the latent function can be derived analytically and the high computational cost caused by approximating the posterior probability with Gaussian distribution is also avoided. The experimental results show that the proposed algorithm can achieve superior classification accuracy to the existing Gaussian process classification algorithms.
出处
《控制与决策》
EI
CSCD
北大核心
2014年第9期1587-1592,共6页
Control and Decision
基金
国家自然科学基金项目(61374170)
国家民委科研项目(12DLZ018
12DLZ001
2013-GM-003)
辽宁省教育厅科学技术研究项目(L2012479
L2013504)
中央高校基本科研业务费专项资金项目(DC13010216
DC120101131)
关键词
高斯过程模型
二分类
后验概率
贝叶斯方法
Gaussian process model
binary classification
posterior probability
Bayesian approach