| bibtype |
J -
Journal Article
|
| ARLID |
0642799 |
| utime |
20251209081835.5 |
| mtime |
20251209235959.9 |
| SCOPUS |
85190739311 |
| WOS |
001202833900001 |
| DOI |
10.14736/kyb-2024-1-0038 |
| title
(primary) (eng) |
Highly Robust Training of Regularized Radial Basis Function Networks |
| specification |
| page_count |
22 s. |
| media_type |
P |
|
| serial |
| ARLID |
cav_un_epca*0297163 |
| ISSN |
0023-5954 |
| title
|
Kybernetika |
| volume_id |
60 |
| volume |
1 (2024) |
| page_num |
38-59 |
| publisher |
| name |
Ústav teorie informace a automatizace AV ČR, v. v. i. |
|
|
| keyword |
effective regularization * * * * |
| keyword |
quantile regression |
| keyword |
regression neural networks |
| keyword |
robust training |
| keyword |
robustness |
| author
(primary) |
| ARLID |
cav_un_auth*0345793 |
| name1 |
Kalina |
| name2 |
Jan |
| institution |
UTIA-B |
| full_dept (cz) |
Stochastická informatika |
| full_dept (eng) |
Department of Stochastic Informatics |
| department (cz) |
SI |
| department (eng) |
SI |
| full_dept |
Department of Stochastic Informatics |
| fullinstit |
Ústav teorie informace a automatizace AV ČR, v. v. i. |
|
| author
|
| ARLID |
cav_un_auth*0416837 |
| name1 |
Vidnerová |
| name2 |
P. |
| country |
CZ |
|
| author
|
| ARLID |
cav_un_auth*0435779 |
| name1 |
Janáček |
| name2 |
P. |
| country |
CZ |
|
| source |
|
| cas_special |
| project |
| project_id |
GA24-10078S |
| agency |
GA ČR |
| country |
CZ |
| ARLID |
cav_un_auth*0472835 |
|
| project |
| project_id |
GA22-02067S |
| agency |
GA ČR |
| country |
CZ |
| ARLID |
cav_un_auth*0435776 |
|
| abstract
(eng) |
Radial basis function (RBF) networks represent established tools for nonlinear regression modeling with numerous applications in various fields. Because their standard training is vulnerable with respect to the presence of outliers in the data, several robust methods for RBF network training have been proposed recently. This paper is interested in robust regularized RBF networks. A robust inter-quantile version of RBF networks based on trimmed least squares is proposed here. Then, a systematic comparison of robust regularized RBF networks follows, which is evaluated over a set of 405 networks trained using various combinations of robustness and regularization types. The experiments proceed with a particular focus on the effect of variable selection, which is performed by means of a backward procedure, on the optimal number of RBF units. The regularized inter-quantile RBF networks based on trimmed least squares turn out to outperform the competing approaches in the experiments if a highly robust prediction error measure is considered. |
| result_subspec |
WOS |
| RIV |
IN |
| FORD0 |
10000 |
| FORD1 |
10200 |
| FORD2 |
10201 |
| reportyear |
2026 |
| inst_support |
RVO:67985556 |
| permalink |
https://hdl.handle.net/11104/0372659 |
| confidential |
S |
| mrcbC91 |
A |
| mrcbT16-e |
COMPUTERSCIENCE.CYBERNETICS |
| mrcbT16-f |
1.1 |
| mrcbT16-g |
0.1 |
| mrcbT16-h |
14.7 |
| mrcbT16-i |
0.00058 |
| mrcbT16-j |
0.288 |
| mrcbT16-k |
978 |
| mrcbT16-q |
43 |
| mrcbT16-s |
0.378 |
| mrcbT16-y |
34 |
| mrcbT16-x |
2.47 |
| mrcbT16-3 |
255 |
| mrcbT16-4 |
Q3 |
| mrcbT16-5 |
2.000 |
| mrcbT16-6 |
43 |
| mrcbT16-7 |
Q3 |
| mrcbT16-C |
40.3 |
| mrcbT16-M |
0.26 |
| mrcbT16-N |
Q4 |
| mrcbT16-P |
40.3 |
| arlyear |
2024 |
| mrcbU14 |
85190739311 SCOPUS |
| mrcbU24 |
PUBMED |
| mrcbU34 |
001202833900001 WOS |
| mrcbU63 |
cav_un_epca*0297163 Kybernetika Roč. 60 č. 1 2024 38 59 0023-5954 Ústav teorie informace a automatizace AV ČR, v. v. i. |
|