Publication
Environmental Data Mapping with Support Vector Regression and Geostatistics
Related publications (41)
Machine learning models for prediction of electrochemical properties in supercapacitor electrodes using MXene and graphene nanoplatelets
Herein, machine learning (ML) models using multiple linear regression (MLR), support vector regression (SVR), random forest (RF) and artificial neural network (ANN) are developed and compared to predict the output features viz. specific capacitance (Csp), ...
Lausanne2024Quantifying the Unknown: Data-Driven Approaches and Applications in Energy Systems
In light of the challenges posed by climate change and the goals of the Paris Agreement, electricity generation is shifting to a more renewable and decentralized pattern, while the operation of systems like buildings is increasingly electrified. This calls ...
EPFL2024Bayes-optimal Learning of Deep Random Networks of Extensive-width
Florent Gérard Krzakala, Lenka Zdeborová, Hugo Chao Cui
We consider the problem of learning a target function corresponding to a deep, extensive-width, non-linear neural network with random Gaussian weights. We consider the asymptotic limit where the number of samples, the input dimension and the network width ...
2023Fast screening of covariates in population models empowered by machine learning
Jan Sickmann Hesthaven, Nadia Terranova
One of the objectives of Pharmacometry (PMX) population modeling is the identification of significant and clinically relevant relationships between parameters and covariates. Here, we demonstrate how this complex selection task could benefit from supervise ...
2021Travel Time Prediction for Congested Freeways With a Dynamic Linear Model
Nikolaos Geroliminis, Semin Kwak
Accurate prediction of travel time is an essential feature to support Intelligent Transportation Systems (ITS). The non-linearity of traffic states, however, makes this prediction a challenging task. Here we propose to use dynamic linear models (DLMs) to a ...
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC2021Real-Time Multi-Ion-Monitoring Front-End With Interference Compensation by Multi-Output Support Vector Regressor
Giovanni De Micheli, Sandro Carrara, Mandresy Ivan Ny Hanitra, Francesca Criscuolo
Ion-sensors play a major role in physiology and healthcare monitoring since they are capable of continuously collecting biological data from body fluids. Nevertheless, ion interference from background electrolytes present in the sample is a paramount chall ...
2021Multi-ion-sensing emulator and multivariate calibration optimization by machine learning models
Giovanni De Micheli, Sandro Carrara, Mandresy Ivan Ny Hanitra, Francesca Criscuolo
One paramount challenge in multi-ion-sensing arises from ion interference that degrades the accuracy of sensor calibration. Machine learning models are here proposed to optimize such multivariate calibration. However, the acquisition of big experimental da ...
2021The association of basic and challenging motor capacity with mobility performance and falls in young seniors
Kamiar Aminian, Anisoara Ionescu
Background: Understanding the association between motor capacity (MC) (what people can do in a standardized environment), mobility performance (MP) (what people actually do in real-life) and falls is important for early detection of and counteracting on fu ...
ELSEVIER IRELAND LTD2020A Global-Local Approach For Detecting Hotspots In Multiple-Response Regression
Anthony Christopher Davison, Jörg Hager, Hélène Ruffieux
We tackle modelling and inference for variable selection in regression problems with many predictors and many responses. We focus on detecting hotspots, that is, predictors associated with several responses. Such a task is critical in statistical genetics, ...
INST MATHEMATICAL STATISTICS2020Automatic L2 Regularization for Multiple Generalized Additive Models
Multiple generalized additive models are a class of statistical regression models wherein parameters of probability distributions incorporate information through additive smooth functions of predictors. The functions are represented by basis function expan ...
EPFL2019