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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 ...
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 ...
It is quite difficult to construct circuits of spiking neurons that can carry out complex computational tasks. On the other hand even randomly connected circuits of spiking neurons can in principle be used for complex computational tasks such as time-warp ...
An in-situ, mid-IR sensor was used to monitor the major analyte concns. involved in the cultivation of Gluconacetobacter xylinus and the prodn. of gluconacetan, a food-grade exopolysaccharide. To predict the analyte concns., three different sets of std. sp ...
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 ...
Risk assessment is in urgent need of more accurate toxic effect endpoints than those currently in use, especially for low concentrations. Often such endpoints are estimated by analysis of variance, linear interpolation, or smoothing. As these statistical m ...
The problem of reconstructing an unknown signal from n noisy samples can be addressed by means of non- parametric estimation techniques such as Tikhonov reg- ularization, Bayesian regression and state-space fixed- interval smoothing. The practical use of t ...
We propose a semiparametric model for regression problems involving multiple response variables. Conditional dependencies between the responses are represented through a linear mixture of Gaussian processes. We propose an efficient approximate inference sc ...
In this paper we present an experimental study in the identification of an industrial hybrid system. Piecewise ARX models, that consist of a number of ARX models, together with the partition of the regressor space into regions where each of the models is v ...
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 ...
