RSMS: Robust Semi-supervised Multi-label Feature Selection for Regression-Reference-Cited by-同舟云学术

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RSMS: Robust Semi-supervised Multi-label Feature Selection for Regression

Published:2023-11-06 Issue: Volume: Page:
ISSN:
Container-title:2023 IEEE 35th International Conference on Tools with Artificial Intelligence (ICTAI)
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Author:

Kraus Vivien¹,Benabdeslem Khalid²,Benkabou Seif-Eddine³,Mansouri Dou El Kefel⁴,Canitia Bruno⁵

Affiliation:

1. Université de Reims Champagne Ardenne,CReSTIC EA 3804,Reims,France,51100

2. University Lyon 1,LIRIS CNRS UMR 5205,Villeurbanne Cedex,France,69622

3. University Poitiers,LIAS-ENSMA,Poitiers,France,86000

4. Ibn Khaldoun University, Tiaret Post box 78,Tiaret,Algeria

5. LIZEO Online Media Group,Lyon,France,69002

Funder

Agence Nationale de la Recherche

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/10356387/10356231/10356467.pdf?arnumber=10356467

Reference36 articles.

1. Convex and scalable weakly labeled svms;Li;The Journal of Machine Learning Research,2013

2. Semi-Supervised Learning Using Gaussian Fields and Harmonic Functions;Zhu

3. Learning from normalized local and global discriminative information for semi-supervised regression and dimensionality reduction

4. Semi-Supervised Learning

5. Constrained Clustering

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