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.
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）.
