LSTSVR+: Least square twin support vector regression with privileged information-Reference-Cited by-同舟云学术

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LSTSVR+: Least square twin support vector regression with privileged information

Published:2024-10 Issue: Volume:136 Page:108964
ISSN:0952-1976
Container-title:Engineering Applications of Artificial Intelligence
language:en
Short-container-title:Engineering Applications of Artificial Intelligence

Author:

Kumari Anuradha^ORCID,Tanveer M.^ORCID

Funder

CSIR

Science and Engineering Research Board

Publisher

Elsevier BV

Reference58 articles.

1. Rademacher and Gaussian complexities: Risk bounds and structural results;Bartlett;J. Mach. Learn. Res.,2002

2. A geometric approach to support vector regression;Bi;Neurocomputing,2003

3. Uniqueness of the SVM solution;Burges;Adv. Neural Inf. Process. Syst.,1999

4. Twin support vector machines with privileged information;Che;Inform. Sci.,2021

5. A robust algorithm of multiquadric method based on an improved huber loss function for interpolating remote-sensing-derived elevation data sets;Chen;Remote Sens.,2015

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