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 "1-versus-1-versus-rest" 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 "1-versus-1-versus-rest" 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
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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
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