On the Impact of Kernel Approximation on Learning Accuracy

@inproceedings{Cortes2010OnTI,
  title={On the Impact of Kernel Approximation on Learning Accuracy},
  author={Corinna Cortes and Mehryar Mohri and Ameet Talwalkar},
  booktitle={AISTATS},
  year={2010}
}
Kernel approximation is commonly used to scale kernel-based algorithms to applications containing as many as several million instances. This paper analyzes the effect of such approximations in the kernel matrix on the hypothesis generated by several widely used learning algorithms. We give stability bounds based on the norm of the kernel approximation for these algorithms, including SVMs, KRR, and graph Laplacian-based regularization algorithms. These bounds help determine the degree of… CONTINUE READING