Gaussian process Machine Learning based ITO algorithm

被引：0

作者：

Ma, Chuang ^{[1
]}

Yang, Yongjian ^{[1
]}

Du, Zhanwei ^{[1
]}

Zhang, Chijun ^{[2
]}

机构：

[1] Jilin Univ, Coll Comp Sci & Technol, Changchun, Peoples R China

[2] Jilin Univ Finance & Econ, Coll Management Sci & Informat Engn, Changchun, Peoples R China

来源：

2014 NINTH INTERNATIONAL CONFERENCE ON BROADBAND AND WIRELESS COMPUTING, COMMUNICATION AND APPLICATIONS (BWCCA) | 2014年

关键词：

Gaussian process; ITO; fluctuation ratio; incremental inheritance; category theory; CLASSIFICATION;

D O I：

10.1109/BWCCA.2014.43

中图分类号：

TM [电工技术]; TN [电子技术、通信技术];

学科分类号：

0808 ; 0809 ;

摘要：

Taking the Gaussian process (GP) regression model as ITO's fluctuation operator, we propose a new mixed algorithm called GITO in order to overcome the local minima problem. Through learning the particles' mobility models, ITO's capacity of local searching and global searching is strengthened. Meanwhile, we give the proof procedure to verify ITO's fluctuation operator and GP are logically equivalent. Finally, the experiments show GITO's better convergence rate and performance.

引用

页码：38 / 41

页数：4

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