Assortment optimization is a core topic of demand management that finds application in a
broad set of different areas including retail, airline, hotel, and transportation industries as …

Since its inception, the choice modelling field has been dominated by theory-driven
modelling approaches. Machine learning offers an alternative data-driven approach for …

Abstract Background Discrete choice models (DCMs) for moral choice analysis will likely
lead to erroneous model outcomes and misguided policy recommendations, as only some …

Since its inception, the choice modelling field has been dominated by theory-driven models.
The recent emergence and growing popularity of machine learning models offer an …

Determining appropriate utility specifications for discrete choice models is time-consuming
and prone to errors. With the availability of larger and larger datasets, as the number of …

This study was conducted to enhance the efficiency of chemical process systems and
address the limitations of conventional methods through hyperparameter optimization …

Preference modelling lies at the intersection of economics, decision theory, machine
learning and statistics. By understanding individuals' preferences and how they make …

In the context of discrete choice modeling, the extraction of potential behavioral insights from
large datasets is often limited by the poor scalability of maximum likelihood estimation. This …

Résumé In the field of choice modeling, the availability of ever-larger datasets has the
potential to significantly expand our understanding of human behavior, but this prospect is …

目的建立了一种结合图像分析和深度学习的油菜籽中总酚含量的快速预测方法.
方法利用VGG19 网络进行油菜籽图像籽粒特征的提取, 通过多个卷积层来学习油菜籽图像的 …