Lorenzo Rosasco

## Description

In this class we show how a large class of techniques for finding stable solutions to matrix inversion problems give rise to consistent kernel methods. Regularized least-squares can be shown to be an instance from this general class of techniques.## Slides

Slides for this lecture: PDF.## Suggested Reading

- Mosci, S., Rosasco, L. and Verri A. " Dimensionality reduction and generalization ", ACM International Conference Proceeding Series; Vol. 227 archive Proceedings of the 24th International Conference on Machine Learning
- Yao Y., Rosasco L. and Caponnetto, A. "On Early Stopping in Gradient Descent Learning", to be published in Constructive Approximation.
- Lo Gerfo L., Rosasco L., Odone F., De Vito E. and Verri, A. Spectral Algorithms for Supervised Learning, to appear in Neural Computation.
- Bauer F., Pereverzev S. and Rosasco L. "On Regularization Algorithms in Learning Theory", J. Complexity 23(1): 52-72 (2007) (Technical Report DISI-TR-05-19).