An improved multi-task least squares twin support vector machine

Kód výsledku v 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>

Výsledek na webu

<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>

Jazyk výsledku

angličtina

Název v původním jazyce

An improved multi-task least squares twin support vector machine

Popis výsledku v původním jazyce

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&apos;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.

Název v anglickém jazyce

An improved multi-task least squares twin support vector machine

Popis výsledku anglicky

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&apos;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.

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

"neuvedeno"

Stát vydavatele periodika

NL - Nizozemsko

Počet stran výsledku

21

Strana od-do

"nestrankovano"

Kód UT WoS článku

001037345700001

EID výsledku v databázi Scopus

2-s2.0-85165955807