Inverse Free Universum Twin Support Vector Machine

Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216208%3A11320%2F21%3A10437045" target="_blank" >RIV/00216208:11320/21:10437045 - isvavai.cz</a>
Result on the web
<a href="https://doi.org/10.1007/978-3-030-92121-7_21" target="_blank" >https://doi.org/10.1007/978-3-030-92121-7_21</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1007/978-3-030-92121-7_21" target="_blank" >10.1007/978-3-030-92121-7_21</a>

Result language
angličtina
Original language name
Inverse Free Universum Twin Support Vector Machine
Original language description
Universum twin support vector machine (U -TSVM) is an efficient method for binary classification problems. In this paper, we improve the U -TSVM algorithm and propose an improved Universum twin bounded support vector machine (named as IUTBSVM). Indeed, by introducing a different Lagrangian function for the primal problems, we obtain new dual formulations so that we do not need to compute inverse matrices. Also to reduce the computational time of the proposed method, we suggest smaller size of the rectangular kernel matrices than the other methods. Numerical experiments on several UCI benchmark data sets indicate that the IUTBSVM is more efficient than the other three algorithms, namely U -SVM, TSVM, and U -TSVM in sense of the classification accuracy. (C) 2021, Springer Nature Switzerland AG.
Czech name
—
Czech description
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Project
<a href="/en/project/GA18-04735S" target="_blank" >GA18-04735S: Novel approaches for relaxation and approximation techniques in deterministic global optimization</a><br>
Continuities
P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Publication year
2021
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

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