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Autori principali: Weinberger, Simón, Cugliari, Jairo, Cain, Aurélie Le
Natura: Preprint
Pubblicazione: 2025
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Accesso online:https://arxiv.org/abs/2506.18615
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author Weinberger, Simón
Cugliari, Jairo
Cain, Aurélie Le
author_facet Weinberger, Simón
Cugliari, Jairo
Cain, Aurélie Le
contents We present a prediction framework for ordinal models: we introduce optimal predictions using loss functions and give the explicit form of the Least-Absolute-Deviation prediction for these models. Then, we reformulate an ordinal model with functional covariates to a classic ordinal model with multiple scalar covariates. We illustrate all the proposed methods and try to apply these to a dataset collected by EssilorLuxottica for the development of a control algorithm for the shade of connected glasses.
format Preprint
id arxiv_https___arxiv_org_abs_2506_18615
institution arXiv
publishDate 2025
record_format arxiv
spellingShingle Pr{é}diction optimale pour un mod{è}le ordinal {à} covariables fonctionnelles
Weinberger, Simón
Cugliari, Jairo
Cain, Aurélie Le
Machine Learning
We present a prediction framework for ordinal models: we introduce optimal predictions using loss functions and give the explicit form of the Least-Absolute-Deviation prediction for these models. Then, we reformulate an ordinal model with functional covariates to a classic ordinal model with multiple scalar covariates. We illustrate all the proposed methods and try to apply these to a dataset collected by EssilorLuxottica for the development of a control algorithm for the shade of connected glasses.
title Pr{é}diction optimale pour un mod{è}le ordinal {à} covariables fonctionnelles
topic Machine Learning
url https://arxiv.org/abs/2506.18615