An oracle inequality for regularized risk minimizers with strongly mixing observations  

作　　者：Feilong CAO Xing XING 

机构地区：[1]Institute of Metrology and Computational Science, China Jiliang University,Hangzhou 310018, China

出　　处：《Frontiers of Mathematics in China》2013年第2期301-315,共15页中国高等学校学术文摘·数学（英文）

基　　金：Acknowledgements The authors would like to express their sincere gratitude to the two anonymous referees for their value comments and suggestions. This work was supported by the National Natural Science Foundation of China （Grant Nos. 61272023, 61101240）.

摘　　要：We establish a general oracle inequality for regularized risk minimizers with strongly mixing observations, and apply this inequality to support vector machine （SVM） type algorithms. The obtained main results extend the previous known results for independent and identically distributed samples to the case of exponentially strongly mixing observations.We establish a general oracle inequality for regularized risk minimizers with strongly mixing observations, and apply this inequality to support vector machine （SVM） type algorithms. The obtained main results extend the previous known results for independent and identically distributed samples to the case of exponentially strongly mixing observations.

关 键 词：Oracle inequality exponentially strongly mixing regularized risk minimizer 

分 类 号：TP311.13[自动化与计算机技术—计算机软件与理论] TV148[自动化与计算机技术—计算机科学与技术]