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Least squares approach to K-SVCR multi-class classification with its applications

The result's identifiers

  • Result code in IS VaVaI

    <a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216208%3A11320%2F22%3A10453335" target="_blank" >RIV/00216208:11320/22:10453335 - isvavai.cz</a>

  • Result on the web

    <a href="https://verso.is.cuni.cz/pub/verso.fpl?fname=obd_publikace_handle&handle=jpsldcMcJG" target="_blank" >https://verso.is.cuni.cz/pub/verso.fpl?fname=obd_publikace_handle&handle=jpsldcMcJG</a>

  • DOI - Digital Object Identifier

    <a href="http://dx.doi.org/10.1007/s10472-021-09747-1" target="_blank" >10.1007/s10472-021-09747-1</a>

Alternative languages

  • Result language

    angličtina

  • Original language name

    Least squares approach to K-SVCR multi-class classification with its applications

  • Original language description

    The support vector classification-regression machine for K-class classification (K-SVCR) is a novel multi-class classification method based on the &quot;1-versus-1-versus-rest&quot; structure. In this paper, we propose a least squares version of K-SVCR named LSK-SVCR. Similarly to the K-SVCR algorithm, this method assesses all the training data into a &quot;1-versus-1-versus-rest&quot; structure, so that the algorithm generates ternary outputs {- 1,0,+ 1}. In LSK-SVCR, the solution of the primal problem is computed by solving only one system of linear equations instead of solving the dual problem, which is a convex quadratic programming problem in K-SVCR. Experimental results on several benchmark, MC-NDC, and handwritten digit recognition data sets show that not only does the LSK-SVCR have better performance in the aspects of classification accuracy to that of K-SVCR and Twin-KSVC algorithms but also has remarkably higher learning speed.

  • Czech name

  • Czech description

Classification

  • Type

    J<sub>imp</sub> - Article in a specialist periodical, which is included in the Web of Science database

  • CEP classification

  • OECD FORD branch

    50201 - Economic Theory

Result continuities

  • 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)

Others

  • Publication year

    2022

  • 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

    90

  • Issue of the periodical within the volume

    7-9

  • Country of publishing house

    NL - THE KINGDOM OF THE NETHERLANDS

  • Number of pages

    20

  • Pages from-to

    873-892

  • UT code for WoS article

    000663987900001

  • EID of the result in the Scopus database

    2-s2.0-85123104517