We found a match
Your institution may have access to this item. Find your institution then sign in to continue.
- Title
Spectral algorithms for supervised learning.
- Authors
Lo Gerfo, L; Rosasco, L; Odone, F; De Vito, E; Verri, A
- Abstract
We discuss how a large class of regularization methods, collectively known as spectral regularization and originally designed for solving ill-posed inverse problems, gives rise to regularized learning algorithms. All of these algorithms are consistent kernel methods that can be easily implemented. The intuition behind their derivation is that the same principle allowing for the numerical stabilization of a matrix inversion problem is crucial to avoid overfitting. The various methods have a common derivation but different computational and theoretical properties. We describe examples of such algorithms, analyze their classification performance on several data sets and discuss their applicability to real-world problems.
- Publication
Neural computation, 2008, Vol 20, Issue 7, p1873
- ISSN
1530-888X
- Publication type
Research
- DOI
10.1162/neco.2008.05-07-517