A Precise Hard-Cut EM Algorithm for Mixtures of Gaussian Processes

被引：0

作者：

Chen, Ziyi ^{[1
]}

Ma, Jinwen ^{[1
]}

Zhou, Yatong ^{[1
]}

机构：

[1] Peking Univ, Sch Math Sci, Dept Informat Sci, Beijing 100871, Peoples R China

来源：

INTELLIGENT COMPUTING METHODOLOGIES | 2014年 / 8589卷

关键词：

Mixture of Gaussian process; Parameter learning; EM algorithm;

D O I：

暂无

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

The mixture of Gaussian processes (MGP) is a powerful framework for machine learning. However, its parameter learning or estimation is still a very challenging problem. In this paper, a precise hard-cut EM algorithm is proposed for learning the parameters of the MGP without any approximation in the derivation. It is demonstrated by the experimental results that our proposed hard-cut EM algorithm for MGP is feasible and even outperforms two available hard-cut EM algorithms.

引用

页码：68 / 75

页数：8

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