#### Filter Results:

- Full text PDF available (53)

#### Publication Year

1989

2018

- This year (7)
- Last 5 years (18)
- Last 10 years (31)

#### Publication Type

#### Co-author

#### Journals and Conferences

Learn More

- Edgar Osuna, Robert Freund, Federico Girosi
- CVPR
- 1997

We investigate the application of Support Vector Machines (SVMs) in computer vision. SVM is a learning technique developed by V. Vapnik and his team (AT&T Bell Labs.) that can be seen as a new methodâ€¦ (More)

- Federico Girosi, Michael Jones, Tomaso A. Poggio
- Neural Computation
- 1995

We had previously shown that regularization principles lead to approximation schemes that are equivalent to networks with one layer of hidden units, called regularization networks. In particular,â€¦ (More)

- Tomaso A. Poggio, Federico Girosi
- Science
- 1990

Learning an input-output mapping from a set of examples, of the type that many neural networks have been constructed to perform, can be regarded as synthesizing an approximation of a multidimensionalâ€¦ (More)

- Federico Girosi
- Neural Computation
- 1998

This article shows a relationship between two different approximation techniques: the support vector machines (SVM), proposed by V. Vapnik (1995) and a sparse approximation scheme that resembles theâ€¦ (More)

- Richard Hillestad, James Bigelow, +4 authors Roger Taylor
- Health affairs
- 2005

To broadly examine the potential health and financial benefits of health information technology (HIT), this paper compares health care with the use of IT in other industries. It estimates potentialâ€¦ (More)

- Bernhard SchÃ¶lkopf, Kah Kay Sung, +4 authors Vladimir Vapnik
- IEEE Trans. Signal Processing
- 1997

The support vector (SV) machine is a novel type of learning machine, based on statistical learning theory, which contains polynomial classifiers, neural networks, and radial basis function (RBF)â€¦ (More)

- Davi Geiger, Federico Girosi
- IEEE Trans. Pattern Anal. Mach. Intell.
- 1991

- Federico Girosi, Tomaso A. Poggio
- Biological Cybernetics
- 2004

Networks can be considered as approximation schemes. Multilayer networks of the perceptron type can approximate arbitrarily well continuous functions (Cybenko 1988, 1989; Funahashi 1989; Stinchcombeâ€¦ (More)

- Partha Niyogi, Federico Girosi
- Neural Computation
- 1996

Feedforward networks together with their training algorithms are a class of regression techniques that can be used to learn to perform some task from a set of examples. The question of generalizationâ€¦ (More)

- Partha Niyogi, Federico Girosi
- Adv. Comput. Math.
- 1999

We consider the problem of approximating functions from scattered data using linear superpositions of non-linearly parameterized functions. We show how the total error (generalization error) can beâ€¦ (More)