#### Filter Results:

- Full text PDF available (41)

#### Publication Year

2007

2017

#### Publication Type

#### Co-author

#### Publication Venue

#### Key Phrases

Learn More

- Giorgio Gnecco, Marcello Sanguineti
- Neural Computation
- 2010

Various regularization techniques are investigated in supervised learning from data. Theoretical features of the associated optimization problems are studied, and sparse suboptimal solutions are searched for. Rates of approximate optimization are estimated for sequences of suboptimal solutions formed by linear combinations of n-tuples of computational… (More)

- Giorgio Gnecco, Marcello Sanguineti
- Comput. Manag. Science
- 2009

- Giorgio Gnecco
- J. Applied Mathematics
- 2012

- Mohammed M. Abdelsamea, Giorgio Gnecco, Mohamed Medhat Gaber
- Neurocomputing
- 2015

Keywords: Region-based segmentation Variational level set method Active contours Self-organizing neurons Region-based prior knowledge a b s t r a c t Active Contour Models (ACMs) constitute a powerful energy-based minimization framework for image segmentation, based on the evolution of an active contour. Among ACMs, supervised ACMs are able to exploit the… (More)

Approximation properties of some connectionistic models, commonly used to construct approximation schemes for optimization problems with multivariable functions as admissible solutions, are investigated. Such models are made up of linear combinations of computational units with adjustable parameters. The relationship between model complexity (number of… (More)

- Giorgio Gnecco, Marco Gori, Marcello Sanguineti
- Neural Computation
- 2013

Kernel machines traditionally arise from an elegant formulation based on measuring the smoothness of the admissible solutions by the norm in the reproducing kernel Hilbert space (RKHS) generated by the chosen kernel. It was pointed out that they can be formulated in a related functional framework, in which the Green's function of suitable differential… (More)

- Giorgio Gnecco, Vera Kurková, Marcello Sanguineti
- Neural Networks
- 2011

Neural networks provide a more flexible approximation of functions than traditional linear regression. In the latter, one can only adjust the coefficients in linear combinations of fixed sets of functions, such as orthogonal polynomials or Hermite functions, while for neural networks, one may also adjust the parameters of the functions which are being… (More)

- Giorgio Gnecco, Marco Gori, Stefano Melacci, Marcello Sanguineti
- Neural Computation
- 2015

The mathematical foundations of a new theory for the design of intelligent agents are presented. The proposed learning paradigm is centered around the concept of constraint, representing the interactions with the environment, and the parsimony principle. The classical regularization framework of kernel machines is naturally extended to the case in which the… (More)

- Giorgio Gnecco, Marcello Sanguineti
- IEEE Transactions on Information Theory
- 2011

A variational norm associated with sets of computational units and used in function approximation, learning from data, and infinite-dimensional optimization is investigated. For sets Gk obtained by varying a vector y of parameters in a fixed-structure computational unit K(-,y) (e.g., the set of Gaussians with free centers and widths), upper and lower bounds… (More)

- Giorgio Gnecco, Vera Kurková, Marcello Sanguineti
- Neural Networks
- 2011

Approximation capabilities of two types of computational models are explored: dictionary-based models (i.e., linear combinations of n-tuples of basis functions computable by units belonging to a set called "dictionary") and linear ones (i.e., linear combinations of n fixed basis functions). The two models are compared in terms of approximation rates, i.e.,… (More)