Efficient Batch and Online Kernel Ridge Regression for Green Clouds

Conference paper
Part of the Lecture Notes on Data Engineering and Communications Technologies book series (LNDECT, volume 1)


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.


Support Vector Machine Cloud Server Incremental Learning Ridge Parameter Kernel Ridge Regression 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  1. 1.School of Information TechnologyMonash UniversityMelbourneAustralia
  2. 2.Department of Media SoftwareSungkyul UniversityAnyang-siSouth Korea
  3. 3.Department of Computer Science and Computer EngineeringLa Trobe UniversityMelbourneAustralia

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