José Ángel Martín Baos
José Ángel Martín Baos
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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
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