An improved multi-task least squares twin support vector machine
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F44555601%3A13440%2F23%3A43897768" target="_blank" >RIV/44555601:13440/23:43897768 - isvavai.cz</a>
Result on the web
<a href="https://link.springer.com/article/10.1007/s10472-023-09877-8" target="_blank" >https://link.springer.com/article/10.1007/s10472-023-09877-8</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1007/s10472-023-09877-8" target="_blank" >10.1007/s10472-023-09877-8</a>
Alternative languages
Result language
angličtina
Original language name
An improved multi-task least squares twin support vector machine
Original language description
In recent years, multi-task learning (MTL) has become a popular field in machine learning and has a key role in various domains. Sharing knowledge across tasks in MTL can improve the performance of learning algorithms and enhance their generalization capability. A new approach called the multi-task least squares twin support vector machine (MTLS-TSVM) was recently proposed as a least squares variant of the direct multi-task twin support vector machine (DMTSVM). Unlike DMTSVM, which solves two quadratic programming problems, MTLS-TSVM solves two linear systems of equations, resulting in a reduced computational time. In this paper, we propose an enhanced version of MTLS-TSVM called the improved multi-task least squares twin support vector machine (IMTLS-TSVM). IMTLS-TSVM offers a significant advantage over MTLS-TSVM by operating based on the empirical risk minimization principle, which allows for better generalization performance. The model achieves this by including regularization terms in its objective function, which helps control the model's complexity and prevent overfitting. We demonstrate the effectiveness of IMTLS-TSVM by comparing it to several single-task and multi-task learning algorithms on various real-world data sets. Our results highlight the superior performance of IMTLS-TSVM in addressing multi-task learning problems.
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
2023
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Name of the periodical
Annals of mathematics and artificial intelligence
ISSN
1012-2443
e-ISSN
1573-7470
Volume of the periodical
2023
Issue of the periodical within the volume
"neuvedeno"
Country of publishing house
NL - THE KINGDOM OF THE NETHERLANDS
Number of pages
21
Pages from-to
"nestrankovano"
UT code for WoS article
001037345700001
EID of the result in the Scopus database
2-s2.0-85165955807