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Bibliografie

Abstract

General framework for binary nonlinear classification on top samples

Mácha Václav, Adam Lukáš, Šmídl Václav

: Book of Abstracts of the 3rd International Conference and Summer School, Numerical Computations: Theory and Algorithms, p. 206-206 , Eds: Sergeyev Yaroslav D., Kvasov Dmitri E., Mukhametzhanov Marat S., Nasso Maria Chiara

: NUMERICAL COMPUTATIONS: THEORY AND ALGORITHMS (NUMTA–2019), (Le Castella Village, IT, 20190615)

: GA18-21409S, GA ČR

: binary classification, duality, kernels, accuracy at the top, ranking, hypothesis testing

: http://library.utia.cas.cz/separaty/2019/AS/macha-0519654.pdf

(eng): 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\nnot use kernels. This forced a restriction on only linear classifiers.

: BB

: 10201