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Improving sample and feature selection with principal covariates regression

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Hierarchical Penalization

Hierarchical penalization is a generic framework for incorporating prior information in the fitting of statistical models, when the explicative variables are organized in a hierarchical structure. The penalizer is a convex functional that performs soft sel ...

2007

Hierarchical Penalization

IDIAP2007

The analysis of kernel ridge regression learning algorithm.

Alexei Pozdnoukhov

The paper presents Kernel Ridge Regression, a nonlinear extension of the well known statistical model of ridge regression. New insights on the method are also presented. In particular, the connection between ridge regression and local translation-invariant ...

IDIAP2002

A New Learning Method for Piecewise Linear Regression

Giancarlo Ferrari Trecate

A new connectionist model for the solution of piecewise lin- ear regression problems is introduced; it is able to reconstruct both con- tinuous and non continuous real valued mappings starting from a finite set of possibly noisy samples. The approximating ...

Springer-Verlag, Heidelberg2002

Computing the equivalent number of parameters of fixed-interval smoothers

Giancarlo Ferrari Trecate

The problem of reconstructing an unknown signal from

n

noisy samples can be addressed by means of nonparametric estimation techniques such as Tikhonov regularization, Bayesian regression and state-space fixed-interval smoothing. The practical use of thes ...

2001

Bayesian methods for nonparametric regression with neural networks

Giancarlo Ferrari Trecate

A regression problem amounts to the reconstruction of a multi-dimensional hypersurface from a finite number of noisy samples. In modern engineering regression algorithms play a fundamental role due to their capability of inferring mathematical models of ph ...

1999

Robust regression through robust covariances

Stephan Morgenthaler

This paper discusses the estimation of regression parameters after summarizing the data by a covariance matrix of the concatenated vector of explanatory variables and response variable. A robust estimate of the covariance matrix leads to a robust regressio ...

1986

Computing the equivalent number of parameters of fixed-interval smoothers

Giancarlo Ferrari Trecate

The problem of reconstructing an unknown signal from

n

2001