DISCRETIZATION ERROR ANALYSIS FOR TIKHONOV REGULARIZATION

By Ernesto De Vito, Lorenzo Rosasco, Andrea Caponnetto. 2006

38 citations recorded by the source corpus.

Subfields: Applied Mathematics, Computational Mechanics, Mathematical Physics

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Applied Mathematics

1. Lorenzo Rosasco
2. Andrea Caponnetto
3. Alessandro Verri
4. Ernesto De Vito
5. Mikhail Belkin
6. Francesca Odone
7. Francis R. Bach
8. Alessandro Rudi

In Computational Mechanics

1. Lorenzo Rosasco
2. Ernesto De Vito
3. Alessandro Verri
4. Andrea Caponnetto
5. Francesca Odone
6. L. Lo Gerfo
7. Francis R. Bach
8. Mikhail Belkin

In Mathematical Physics

1. Lorenzo Rosasco
2. Alessandro Verri
3. Andrea Caponnetto
4. Ernesto De Vito
5. Mikhail Belkin
6. Francesca Odone
7. L. Lo Gerfo
8. Francis R. Bach

Citations in the corpus

• Optimal Rates for Regularization Operators in Learning Theory (2006, methodology): “Regularization methods have been recently studied in the context of learning theory in [13, 9, 8, 10, 1].”
• Spectral Algorithms for Supervised Learning (2008, background): “A mathematical connection between sampling theory and learning theory has been recently proposed by Smale and Zhou (2004, 2005) whereas De Vito, Rosasco, Caponnetto, et al. (2005) and De Vito et al. (2006) gave an inverse problem perspective on learning.”
• On Learning with Integral Operators (2010, background): “The proof of the following proposition can be deduced from the resultsin De Vito et al. (2005b) and De Vito et al. (2006).3 Proposition 8 The following facts hold true.”
• Spectral Methods for Regularization in Learning Theory (2006, methodology): “In this section we clarify the role of the regularization looking at learning algorithm as an inverse problem as showed in De Vito et al. (2005b).”