Enhanced Multi-Task Learning Using Optimization Methods
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F44555601%3A13440%2F25%3A43899196" target="_blank" >RIV/44555601:13440/25:43899196 - isvavai.cz</a>
Result on the web
<a href="https://link.springer.com/article/10.1007/s41060-025-00859-z" target="_blank" >https://link.springer.com/article/10.1007/s41060-025-00859-z</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1007/s41060-025-00859-z" target="_blank" >10.1007/s41060-025-00859-z</a>
Alternative languages
Result language
angličtina
Original language name
Enhanced Multi-Task Learning Using Optimization Methods
Original language description
This paper presents an improved multi-task twin support vector machine method utilizing Universum data, referred to as I-UMTSVM. A significant advancement of I-UMTSVM over UMTSVM is the addition of a regularization term, which facilitates the application of the structural risk minimization principle. This modification captures the essence of statistical learning theory and leads to enhanced classification performance. Two approaches are proposed for solving the I-UMTSVM problem. The first is a dual formulation of I-UMTSVM, which involves solving a quadratic programming problem. The second, NI-UMTSVM, is a Newton-based approach that addresses I-UMTSVM in the primal space. In NI-UMTSVM, the constrained optimization problems of I-UMTSVM are transformed into unconstrained ones, and a generalized Newton's method is introduced to solve them effectively. The efficiency of the proposed methods is demonstrated through numerical experiments on various benchmark multi-task data sets. These experiments provide evidence of the effectiveness and strong performance of the proposed approaches in multi-task learning tasks.
Czech name
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Czech description
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Classification
Type
J<sub>imp</sub> - Article in a specialist periodical, which is included in the Web of Science database
CEP classification
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OECD FORD branch
10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)
Result continuities
Project
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Continuities
I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace
Others
Publication year
2025
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Name of the periodical
International Journal of Data Science and Analytics
ISSN
2364-415X
e-ISSN
2364-4168
Volume of the periodical
2025
Issue of the periodical within the volume
"necislovano"
Country of publishing house
GB - UNITED KINGDOM
Number of pages
22
Pages from-to
"nestrankovano"
UT code for WoS article
001532968200001
EID of the result in the Scopus database
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