| Abstract: |
| Distributed machine learning is an effective way to process large scale data. In this talk, I will present an introduction to the divide and conquer approach to implement distributed machine learning. We proved the optimality of kernel based machine learning methods for regression analysis and ranking. We proposed a bias correction trick to improve the performance of distributed kernel regression while preserving the theoretical optimality. We also proposed effective strategies for distributed classification by conducting a comparative analysis. |
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