bibtype |
A -
Abstract
|
ARLID |
0519654 |
utime |
20240103223428.0 |
mtime |
20200114235959.9 |
title
(primary) (eng) |
General framework for binary nonlinear classification on top samples |
specification |
page_count |
1 s. |
media_type |
P |
|
serial |
ARLID |
cav_un_epca*0519653 |
ISBN |
9788874581016 |
title
|
Book of Abstracts of the 3rd International Conference and Summer School, Numerical Computations: Theory and Algorithms |
page_num |
206-206 |
publisher |
place |
Rende |
name |
Centro Editoriale e Librario dell’Universit`a della Calabria |
year |
2019 |
|
editor |
name1 |
Sergeyev |
name2 |
Yaroslav D. |
|
editor |
name1 |
Kvasov |
name2 |
Dmitri E. |
|
editor |
name1 |
Mukhametzhanov |
name2 |
Marat S. |
|
editor |
name1 |
Nasso |
name2 |
Maria Chiara |
|
|
keyword |
binary classification |
keyword |
duality |
keyword |
kernels |
keyword |
accuracy at the top |
keyword |
ranking |
keyword |
hypothesis testing |
author
(primary) |
ARLID |
cav_un_auth*0387255 |
name1 |
Mácha |
name2 |
Václav |
institution |
UTIA-B |
full_dept (cz) |
Adaptivní systémy |
full_dept (eng) |
Adaptive Systems |
department (cz) |
AS |
department (eng) |
AS |
country |
CZ |
fullinstit |
Ústav teorie informace a automatizace AV ČR, v. v. i. |
|
author
|
ARLID |
cav_un_auth*0309054 |
name1 |
Adam |
name2 |
Lukáš |
institution |
UTIA-B |
full_dept (cz) |
Matematická teorie rozhodování |
full_dept |
Department of Decision Making Theory |
department (cz) |
MTR |
department |
MTR |
full_dept |
Department of Decision Making Theory |
country |
CZ |
fullinstit |
Ústav teorie informace a automatizace AV ČR, v. v. i. |
|
author
|
ARLID |
cav_un_auth*0101207 |
name1 |
Šmídl |
name2 |
Václav |
institution |
UTIA-B |
full_dept (cz) |
Adaptivní systémy |
full_dept |
Adaptive Systems |
department (cz) |
AS |
department |
AS |
full_dept |
Department of Adaptive Systems |
fullinstit |
Ústav teorie informace a automatizace AV ČR, v. v. i. |
|
source |
|
cas_special |
project |
ARLID |
cav_un_auth*0374053 |
project_id |
GA18-21409S |
agency |
GA ČR |
|
abstract
(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. |
action |
ARLID |
cav_un_auth*0387256 |
name |
NUMERICAL COMPUTATIONS: THEORY AND ALGORITHMS (NUMTA–2019) |
dates |
20190615 |
mrcbC20-s |
20190621 |
place |
Le Castella Village |
country |
IT |
|
RIV |
BB |
FORD0 |
10000 |
FORD1 |
10200 |
FORD2 |
10201 |
reportyear |
2020 |
mrcbC52 |
4 O 4o 20231122144638.3 |
presentation_type |
PR |
inst_support |
RVO:67985556 |
permalink |
http://hdl.handle.net/11104/0304787 |
confidential |
S |
arlyear |
2019 |
mrcbTft |
\nSoubory v repozitáři: 0519654.pdf |
mrcbU14 |
SCOPUS |
mrcbU24 |
PUBMED |
mrcbU34 |
WOS |
mrcbU63 |
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 |
mrcbU67 |
340 Sergeyev Yaroslav D. |
mrcbU67 |
340 Kvasov Dmitri E. |
mrcbU67 |
340 Mukhametzhanov Marat S. |
mrcbU67 |
340 Nasso Maria Chiara |
|