A Novel Method for Solving Universum Twin Bounded Support Vector Machine in the Primal Space
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F44555601%3A13440%2F23%3A43897715" target="_blank" >RIV/44555601:13440/23:43897715 - isvavai.cz</a>
Result on the web
<a href="https://link.springer.com/article/10.1007/s10472-023-09871-0" target="_blank" >https://link.springer.com/article/10.1007/s10472-023-09871-0</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1007/s10472-023-09871-0" target="_blank" >10.1007/s10472-023-09871-0</a>
Alternative languages
Result language
angličtina
Original language name
A Novel Method for Solving Universum Twin Bounded Support Vector Machine in the Primal Space
Original language description
In supervised learning, the Universum, a third class that is not a part of either class in the classification task, has proven to be useful. In this study we propose (NUTBSVM), a Newton based approach for solving in the primal space the optimization problems related to Twin Bounded Support Vector Machines with Universum data (UTBSVM). In the NUTBSVM, the constrained programming problems of UTBSVM are converted into unconstrained optimization problems, and a generalization of Newton's method for solving the unconstrained problems is introduced. Numerical experiments on synthetic, UCI, and NDC data sets show the ability and effectiveness of the proposed NUTBSVM. We apply the suggested method for gender detection from face images, and compare it with other methods.
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
"neuveden"
Country of publishing house
NL - THE KINGDOM OF THE NETHERLANDS
Number of pages
20
Pages from-to
"nestrankovano"
UT code for WoS article
001022094700001
EID of the result in the Scopus database
2-s2.0-85163719205