Ricardo García-Ródenas

Recientes

• PyKernelLogit: Penalised maximum likelihood estimation of Kernel Logistic Regression in Python
• Scalable Kernel Logistic Regression with Nyström Approximation: Theoretical Analysis and Application to Discrete Choice Modelling
• The time slot allocation problem in liberalised passenger railway markets: a multi-objective approach
• A prediction and behavioural analysis of machine learning methods for modelling travel mode choice
• ROBIN: Rail mOBIlity simulatioN
• The time slots allocation problem in liberalised passenger railway markets: a multi-objective approach
• Nyström-based approximations for kernel logistic regression: Application to transport choice modelling
• Optimization techniques for Kernel Logistic Regression on large-scale datasets: A comparative study
• A comparative study of machine learning, deep neural networks and random utility maximization models for travel mode choice modelling
• IoT based monitoring of air quality and traffic using regression analysis
• A Python package for performing penalized maximum likelihood estimation of conditional logit models using Kernel Logistic Regression
• A comparative study of machine learning, deep neural networks and random utility maximization models for travel mode choice modelling
• A Python package for performing penalized maximum likelihood estimation of conditional logit models using Kernel Logistic Regression
• Revisiting kernel logistic regression under the random utility models perspective. An interpretable machine-learning approach
• A python package for performing penalized maximum likelihood estimation of conditional logit models using kernel logistic regression
• A comparison of general-purpose optimization algorithms for finding optimal approximate experimental designs
• Discrete choice modeling using Kernel Logistic Regression
• Discrete choice modeling using Kernel Logistic Regression
• A methodology for monitoring traffic flow and air pollution in urban areas