Search results for "REGULARIZATION"
showing 10 items of 189 documents
An entropy-based machine learning algorithm for combining macroeconomic forecasts
2019
This paper applies a Machine Learning approach with the aim of providing a single aggregated prediction from a set of individual predictions. Departing from the well-known maximum-entropy inference methodology, a new factor capturing the distance between the true and the estimated aggregated predictions presents a new problem. Algorithms such as ridge, lasso or elastic net help in finding a new methodology to tackle this issue. We carry out a simulation study to evaluate the performance of such a procedure and apply it in order to forecast and measure predictive ability using a dataset of predictions on Spanish gross domestic product.
A machine learning application to predict early lung involvement in scleroderma: A feasibility evaluation
2021
Introduction: Systemic sclerosis (SSc) is a systemic immune-mediated disease, featuring fibrosis of the skin and organs, and has the greatest mortality among rheumatic diseases. The nervous system involvement has recently been demonstrated, although actual lung involvement is considered the leading cause of death in SSc and, therefore, should be diagnosed early. Pulmonary function tests are not sensitive enough to be used for screening purposes, thus they should be flanked by other clinical examinations
The renormalized electron mass in non-relativistic quantum electrodynamics
2007
This work addresses the problem of infrared mass renormalization for a scalar electron in a translation-invariant model of non-relativistic QED. We assume that the interaction of the electron with the quantized electromagnetic field comprises a fixed ultraviolet regularization and an infrared regularization parametrized by $\sigma>0$. For the value $p=0$ of the conserved total momentum of electron and photon field, bounds on the renormalized mass are established which are uniform in $\sigma\to0$, and the existence of a ground state is proved. For $|p|>0$ sufficiently small, bounds on the renormalized mass are derived for any fixed $\sigma>0$. A key ingredient of our proofs is the operator-t…
An equilibrium point regularization for the Chen system
2006
This paper addresses the control of the chaotic Chen system via a feedback technique. We first present a nonlinear feedback controller which drives the trajectories of the Chen system to a given point for any initial conditions. Then, we design a linear feedback controller which still assures the global stability of the Chen system. We moreover achieve the tracking of a reference signal. Numerical simulations are provided to show the effectiveness of the developed controllers.
Scad-elastic net and the estimation of individual tourism expenditure determinants
2014
This paper introduces the use of scad-elastic net in the assessment of the determinants of individual tourist spending. This technique approaches two main estimation-related issues of primary importance. So far studies of tourism literature have made a wide use of classic regressions, whose results might be affected by multicollinearity. In addition, because of the absence of robust economic theory on tourism behavior, regressor selection is often left to researcher's choice when not driven by non-optimal automatic criteria. Scad-elastic net is an OLS model that accounts for both these problems by including two types of parameters constraints, namely the smoothly clipped absolute deviation …
P2D: a self-supervised method for depth estimation from polarimetry
2021
Monocular depth estimation is a recurring subject in the field of computer vision. Its ability to describe scenes via a depth map while reducing the constraints related to the formulation of perspective geometry tends to favor its use. However, despite the constant improvement of algorithms, most methods exploit only colorimetric information. Consequently, robustness to events to which the modality is not sensitive to, like specularity or transparency, is neglected. In response to this phenomenon, we propose using polarimetry as an input for a self-supervised monodepth network. Therefore, we propose exploiting polarization cues to encourage accurate reconstruction of scenes. Furthermore, we…
Efficient Nonlinear RX Anomaly Detectors
2020
Current anomaly detection algorithms are typically challenged by either accuracy or efficiency. More accurate nonlinear detectors are typically slow and not scalable. In this letter, we propose two families of techniques to improve the efficiency of the standard kernel Reed-Xiaoli (RX) method for anomaly detection by approximating the kernel function with either {\em data-independent} random Fourier features or {\em data-dependent} basis with the Nystr\"om approach. We compare all methods for both real multi- and hyperspectral images. We show that the proposed efficient methods have a lower computational cost and they perform similar (or outperform) the standard kernel RX algorithm thanks t…
CLEAR: Covariant LEAst-Square Refitting with Applications to Image Restoration
2017
International audience; In this paper, we propose a new framework to remove parts of the systematic errors affecting popular restoration algorithms, with a special focus for image processing tasks. Generalizing ideas that emerged for $\ell_1$ regularization, we develop an approach re-fitting the results of standard methods towards the input data. Total variation regularizations and non-local means are special cases of interest. We identify important covariant information that should be preserved by the re-fitting method, and emphasize the importance of preserving the Jacobian (w.r.t. the observed signal) of the original estimator. Then, we provide an approach that has a ``twicing'' flavor a…
Characterizing the maximum parameter of the total-variation denoising through the pseudo-inverse of the divergence
2017
International audience; We focus on the maximum regularization parameter for anisotropic total-variation denoising. It corresponds to the minimum value of the regularization parameter above which the solution remains constant. While this value is well know for the Lasso, such a critical value has not been investigated in details for the total-variation. Though, it is of importance when tuning the regularization parameter as it allows fixing an upper-bound on the grid for which the optimal parameter is sought. We establish a closed form expression for the one-dimensional case, as well as an upper-bound for the two-dimensional case, that appears reasonably tight in practice. This problem is d…
On parameterizing thermodynamic descriptions of minerals for petrological calculations
2014
A new regularization approach, termed micro-ϕ, is outlined for parameterizing activity–composition (a–x) relations and other aspects of the thermodynamic descriptions of minerals for petrological calculations. In the context of the symmetric formalism, a formulation of a–x relations that is easily generalizable to multi-component minerals, parameterization with micro-ϕ extends from where there are good data available to constrain, for example, interaction energies, to where there are little or no data. This involves decomposing the interaction energies, which are macroscopic between end-members, into their microscopic components involving interactions between elements on sites. Micro-ϕ invo…