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 for this lecture: PDF
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).