Bounds for sparse solutions of K-SVCR multi-class classification model
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216208%3A11320%2F23%3A10472187" target="_blank" >RIV/00216208:11320/23:10472187 - isvavai.cz</a>
Result on the web
<a href="https://doi.org/10.1007/978-3-031-24866-5_11" target="_blank" >https://doi.org/10.1007/978-3-031-24866-5_11</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1007/978-3-031-24866-5_11" target="_blank" >10.1007/978-3-031-24866-5_11</a>
Alternative languages
Result language
angličtina
Original language name
Bounds for sparse solutions of K-SVCR multi-class classification model
Original language description
The support vector classification-regression machine for k-class classification (K-SVCR) is a novel multi-class classification approach based on the 1-versus-1-versus-rest structure. In this work, we suggested an efficient model by proposing the p-norm (0<p<1) instead of the 2-norm for the regularization term in the objective function of K-SVCR that can be used for feature selection. We derived lower bounds for the absolute value of nonzero entries in every local optimal solution of the p-norm based model. Also, we provided upper bounds for the number of nonzero components of the optimal solutions. We explored the link between solution sparsity, regularization parameters, and the p-choice.
Czech name
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Czech description
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Classification
Type
D - Article in proceedings
CEP classification
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OECD FORD branch
10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)
Result continuities
Project
<a href="/en/project/GA22-11117S" target="_blank" >GA22-11117S: Global sensitivity analysis and stability in optimization problems</a><br>
Continuities
P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)
Others
Publication year
2023
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Article name in the collection
Learning and Intelligent Optimization, 16th International Conference, LION 16
ISBN
978-3-031-24866-5
ISSN
0302-9743
e-ISSN
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Number of pages
9
Pages from-to
136-144
Publisher name
Springer
Place of publication
Cham
Event location
Milos Island, Greece
Event date
Jun 5, 2022
Type of event by nationality
WRD - Celosvětová akce
UT code for WoS article
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