0519654 20240103223428.020200114235959.9 1 s. P cav_un_epca*05196539788874581016206-206RendeCentro Editoriale e Librario dell’Universit`a della Calabria2019SergeyevYaroslav D.KvasovDmitri E.MukhametzhanovMarat S.NassoMaria Chiara binary classification duality kernels accuracy at the top ranking hypothesis testing cav_un_auth*0387255 Mácha Václav UTIA-B Adaptivní systémy Adaptive Systems AS AS CZ Ústav teorie informace a automatizace AV ČR, v. v. i. cav_un_auth*0309054 Adam Lukáš UTIA-B Matematická teorie rozhodování Department of Decision Making Theory MTR MTR Department of Decision Making Theory CZ Ústav teorie informace a automatizace AV ČR, v. v. i. cav_un_auth*0101207 Šmídl Václav UTIA-B Adaptivní systémy Adaptive Systems AS AS Department of Adaptive Systems Ústav teorie informace a automatizace AV ČR, v. v. i. http://library.utia.cas.cz/separaty/2019/AS/macha-0519654.pdf cav_un_auth*0374053 GA18-21409S GA ČR In our previous work [1], we have proposed a general framework to handle binary linear classification for top samples. Our framework includes ranking problems, accuracy at the top or hypothesis testing. We have summarized known methods, such as [2, 3, 4], belonging to this framework and proposed new ones. Note that these methods were either derived in their primal form, or they did not use kernels. This forced a restriction on only linear classifiers. cav_un_auth*0387256 NUMERICAL COMPUTATIONS: THEORY AND ALGORITHMS (NUMTA–2019) 20190615 20190621 Le Castella Village IT BB 10000 10200 10201 2020 4 O 4o 20231122144638.3 PR RVO:67985556 http://hdl.handle.net/11104/0304787 S 2019 Soubory v repozitáři: 0519654.pdf SCOPUS PUBMED WOS cav_un_epca*0519653 Book of Abstracts of the 3rd International Conference and Summer School, Numerical Computations: Theory and Algorithms Centro Editoriale e Librario dell’Universit`a della Calabria 2019 Rende 206 206 9788874581016 340 Sergeyev Yaroslav D. 340 Kvasov Dmitri E. 340 Mukhametzhanov Marat S. 340 Nasso Maria Chiara