Multi-task Support Vector Machine Classifier with Generalized Huber Loss

Qi Liu (), Wenxin Zhu (), Zhengming Dai () and Zhihong Ma ()
Additional contact information
Qi Liu: Tianjin Agricultural University
Wenxin Zhu: Tianjin Agricultural University
Zhengming Dai: Institute of Electronic Standardization Technology
Zhihong Ma: Tianjin Agricultural University

Journal of Classification, 2025, vol. 42, issue 1, No 12, 252 pages

Abstract: Abstract Compared to single-task learning (STL), multi-task learning (MTL) achieves a better generalization by exploiting domain-specific information implicit in the training signals of several related tasks. The adaptation of MTL to support vector machines (SVMs) is a rather successful example. Inspired by the recently published generalized Huber loss SVM (GHSVM) and regularized multi-task learning (RMTL), we propose a novel generalized Huber loss multi-task support vector machine including linear and non-linear cases for binary classification, named as MTL-GHSVM. The new method extends the GHSVM from single-task to multi-task learning, and the application of Huber loss to MTL-SVM is innovative to the best of our knowledge. The proposed method has two main advantages: on the one hand, compared with SVMs with hinge loss and GHSVM, our MTL-GHSVM using the differentiable generalized Huber loss has better generalization performance; on the other hand, it adopts functional iteration to find the optimal solution, and does not need to solve a quadratic programming problem (QPP), which can significantly reduce the computational cost. Numerical experiments have been conducted on fifteen real datasets, and the results demonstrate the effectiveness of the proposed multi-task classification algorithm compared with the state-of-the-art algorithms.

Keywords: Multi-task learning; SVM; Generalized Huber loss; Functional iterative scheme (search for similar items in EconPapers)
Date: 2025
References: Add references at CitEc
Citations:

Downloads: (external link)
http://link.springer.com/10.1007/s00357-024-09488-w Abstract (text/html)
Access to the full text of the articles in this series is restricted.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:spr:jclass:v:42:y:2025:i:1:d:10.1007_s00357-024-09488-w

Ordering information: This journal article can be ordered from
http://www.springer. ... hods/journal/357/PS2

DOI: 10.1007/s00357-024-09488-w

Access Statistics for this article

Journal of Classification is currently edited by Douglas Steinley

More articles in Journal of Classification from Springer, The Classification Society
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().