Efficient batch and online kernel ridge regression for green clouds

Abstract

This study presents an energy-economic approach for incremental/decremental learning based on kernel ridge regression, a frequently used regressor on clouds. To avoid reanalyzing the entire dataset when data change, the proposed mechanism supports incremental/decremental processing for both single and multiple samples (i.e., batch processing). Experimental results showed that the performance in accuracy of the proposed method remained as well as original design. Furthermore, training time was reduced. These findings thereby demonstrate the effectiveness of the proposed method.