Least squares K-SVCR multi-class classification

Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216208%3A11320%2F20%3A10419277" target="_blank" >RIV/00216208:11320/20:10419277 - isvavai.cz</a>
Result on the web
<a href="https://doi.org/10.1007/978-3-030-53552-0_13" target="_blank" >https://doi.org/10.1007/978-3-030-53552-0_13</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1007/978-3-030-53552-0_13" target="_blank" >10.1007/978-3-030-53552-0_13</a>

Result language
angličtina
Original language name
Least squares K-SVCR multi-class classification
Original language description
The support vector classification-regression machine for K-class classification (K-SVCR) is a novel multi-class classification method based on 1-versus-1-versus-rest structure. In this paper, we propose a least squares version of K-SVCR named as LSK-SVCR. Similarly as the K-SVCR algorithm, this method assess all the training data into a 1-versus-1-versus-rest structure, so that the algorithm generates ternary output -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 data set show that the LSK-SVCR has better performance in the aspects of predictive accuracy and learning speed.
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
2020
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

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