Universum parametric-margin ν-support vector machine for classification using the difference of convex functions algorithm
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216208%3A11320%2F21%3A10451766" target="_blank" >RIV/00216208:11320/21:10451766 - isvavai.cz</a>
Result on the web
<a href="https://verso.is.cuni.cz/pub/verso.fpl?fname=obd_publikace_handle&handle=rti~2WpfKr" target="_blank" >https://verso.is.cuni.cz/pub/verso.fpl?fname=obd_publikace_handle&handle=rti~2WpfKr</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1007/s10489-021-02402-6" target="_blank" >10.1007/s10489-021-02402-6</a>
Alternative languages
Result language
angličtina
Original language name
Universum parametric-margin ν-support vector machine for classification using the difference of convex functions algorithm
Original language description
Universum data that do not belong to any class of a classification problem can be exploited to utilize prior knowledge to improve generalization performance. In this paper, we design a novel parametric ν-support vector machine with universum data (UPar-ν-SVM). Unlabeled samples can be integrated into supervised learning by means of UPar-ν-SVM. We propose a fast method to solve the suggested problem of UPar-ν-SVM. The primal problem of UPar-ν-SVM, which is a nonconvex optimization problem, is transformed into an unconstrained optimization problem so that the objective function can be treated as a difference of two convex functions (DC). To solve this unconstrained problem, a boosted difference of convex functions algorithm (BDCA) based on a generalized Newton method is suggested (named DC-UPar-ν-SVM). We examined our approach on UCI benchmark data sets, NDC data sets, a handwritten digit recognition data set, and a landmine detection data set. The experimental results confirmed the effectiveness and superiority of the proposed method for solving classification problems in comparison with other methods. (C) 2021, The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature.
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
2021
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Name of the periodical
Applied Intelligence
ISSN
0924-669X
e-ISSN
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Volume of the periodical
52
Issue of the periodical within the volume
3
Country of publishing house
NL - THE KINGDOM OF THE NETHERLANDS
Number of pages
21
Pages from-to
2634-2654
UT code for WoS article
000662820100004
EID of the result in the Scopus database
2-s2.0-85108106202