Optimal Scaling: New Insights Into an Old Problem - Cnam - Conservatoire national des arts et métiers
Conference Papers Year : 2024

Optimal Scaling: New Insights Into an Old Problem

Gilbert Saporta
CEDRIC. Méthodes statistiques de data-mining et apprentissage
CV

Abstract

Processing qualitative variables with a very large number of categories in Machine Learning is an opportunity to revisit the theory of optimal scaling and its applications

Domains

Statistics [stat] Computer Science [cs]
Fichier principal
Vignette du fichier
PaperSaportaPalerme.pdf (179.63 Ko) Télécharger le fichier
Origin Files produced by the author(s)

Gilbert Saporta  : Connect in order to contact the contributor

https://cnam.hal.science/hal-04544367

Submitted on : Friday, April 12, 2024-5:36:18 PM

Last modification on : Friday, August 23, 2024-12:28:21 PM

Download

Dates and versions

hal-04544367 , version 1 (12-04-2024)

Identifiers

  • HAL Id : hal-04544367 , version 1

Cite

Gilbert Saporta. Optimal Scaling: New Insights Into an Old Problem. SDS 2024; Statistics and Data Science Conference, SIS, Apr 2024, Palermo, Italy. pp.97-100. ⟨hal-04544367⟩

Export

BibTeX XML-TEI Dublin Core DC Terms EndNote DataCite

Collections

CNAM CEDRIC-CNAM HESAM
37 View
26 Download

Share

More