A Novel Method for Solving Universum Twin Bounded Support Vector Machine in the Primal Space

Kód výsledku v 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>

Výsledek na webu

<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>

Jazyk výsledku

angličtina

Název v původním jazyce

A Novel Method for Solving Universum Twin Bounded Support Vector Machine in the Primal Space

Popis výsledku v původním jazyce

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&apos;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.

Název v anglickém jazyce

A Novel Method for Solving Universum Twin Bounded Support Vector Machine in the Primal Space

Popis výsledku anglicky

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&apos;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.

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

—

OECD FORD obor

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Projekt

—

Návaznosti

I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Rok uplatnění

2023

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název periodika

Annals of mathematics and artificial intelligence

ISSN

1012-2443

e-ISSN

1573-7470

Svazek periodika

2023

Číslo periodika v rámci svazku

"neuveden"

Stát vydavatele periodika

NL - Nizozemsko

Počet stran výsledku

20

Strana od-do

"nestrankovano"

Kód UT WoS článku

001022094700001

EID výsledku v databázi Scopus

2-s2.0-85163719205