The success of machine-learning methods is spreading their use to many different fields.
This paper analyses one of these methods, the Kernel Logistic Regression (KLR), from the …

The poor predictability and the misspecification arising from hand-crafted utility functions are
common issues in theory-driven discrete choice models (DCMs). Data-driven DCMs improve …

Several non-linear functions and machine learning methods have been developed for
flexible specification of the systematic utility in discrete choice models. However, they lack …

The application of kernel-based Machine Learning (ML) techniques to discrete choice
modelling using large datasets often faces challenges due to memory requirements and the …

Eliciting individual-level decisions is of interest in multiple disciplines, such as
transportation, economics, environment, ecology, and health, among others. Discrete choice …

The success of machine-learning methods is spreading their use to many different fields.
This paper analyses one of these methods, the Kernel Logistic Regression (KLR), from the …