| bibtype |
J -
Journal Article
|
| ARLID |
0464463 |
| utime |
20240903170538.3 |
| mtime |
20161029235959.9 |
| SCOPUS |
85020286021 |
| WOS |
000388307600001 |
| DOI |
10.14311/NNW.2016.26.024 |
| title
(primary) (eng) |
On-line mixture-based alternative to logistic regression |
| specification |
| page_count |
20 s. |
| media_type |
E |
|
| serial |
| ARLID |
cav_un_epca*0290321 |
| ISSN |
1210-0552 |
| title
|
Neural Network World |
| volume_id |
26 |
| volume |
5 (2016) |
| page_num |
417-437 |
| publisher |
| name |
Ústav informatiky AV ČR, v. v. i. |
|
|
| keyword |
on-line modeling |
| keyword |
on-line logistic regression |
| keyword |
recursive mixture estimation |
| keyword |
data dependent pointer |
| author
(primary) |
| ARLID |
cav_un_auth*0101167 |
| full_dept (cz) |
Zpracování signálů |
| full_dept (eng) |
Department of Signal Processing |
| department (cz) |
ZS |
| department (eng) |
ZS |
| full_dept |
Department of Signal Processing |
| name1 |
Nagy |
| name2 |
Ivan |
| institution |
UTIA-B |
| fullinstit |
Ústav teorie informace a automatizace AV ČR, v. v. i. |
|
| author
|
| ARLID |
cav_un_auth*0108105 |
| full_dept (cz) |
Zpracování signálů |
| full_dept |
Department of Signal Processing |
| department (cz) |
ZS |
| department |
ZS |
| full_dept |
Department of Signal Processing |
| name1 |
Suzdaleva |
| name2 |
Evgenia |
| institution |
UTIA-B |
| country |
RU |
| fullinstit |
Ústav teorie informace a automatizace AV ČR, v. v. i. |
|
| source |
|
| cas_special |
| project |
| ARLID |
cav_un_auth*0321440 |
| project_id |
GA15-03564S |
| agency |
GA ČR |
|
| abstract
(eng) |
The paper deals with a problem of modeling discrete variables depending on continuous variables. This problem is known as the logistic regression estimated by numerical methods. The paper approaches the problem via the recursive Bayesian estimation of mixture models with the purpose of exploring a possibility of constructing the continuous data dependent switching model that should be estimated on-line. Here the model of the discrete variable dependent on continuous data is represented as the model of the mixture pointer dependent on data from mixture components via their parameters, which switch according to the activity of the components. On-line estimation of the data dependent pointer model has a great potential for tasks of clustering and classification. The specific subproblems include (i) the model parameter estimation both of the pointer and of the components obtained during the learning phase, and (ii) prediction of the pointer value during the testing phase. |
| RIV |
BB |
| reportyear |
2017 |
| num_of_auth |
2 |
| inst_support |
RVO:67985556 |
| permalink |
http://hdl.handle.net/11104/0263657 |
| confidential |
S |
| mrcbC86 |
3+4 Article Computer Science Artificial Intelligence |
| mrcbT16-e |
COMPUTERSCIENCEARTIFICIALINTELLIGENCE |
| mrcbT16-j |
0.091 |
| mrcbT16-s |
0.174 |
| mrcbT16-4 |
Q4 |
| mrcbT16-B |
2.277 |
| mrcbT16-D |
Q4 |
| mrcbT16-E |
Q4 |
| arlyear |
2016 |
| mrcbU14 |
85020286021 SCOPUS |
| mrcbU34 |
000388307600001 WOS |
| mrcbU56 |
pdf |
| mrcbU63 |
cav_un_epca*0290321 Neural Network World 1210-0552 Roč. 26 č. 5 2016 417 437 Ústav informatiky AV ČR, v. v. i. |
|