Zobrazeno 1 - 10
of 18
pro vyhledávání: '"Marcell Stippinger"'
Autor:
Bálint Varga, Marcell Stippinger, Fülöp Bazsó, Attila Bencze, Zoltán Somogyvári, László Négyessy, Hisashi Tanigawa, Tamás Kiss
Publikováno v:
IBRO Neuroscience Reports, Vol 15, Iss , Pp S906- (2023)
Externí odkaz:
https://doaj.org/article/ec692cfd2d2840668d6d4721c1555787
Autor:
Zoltán Somogyvári, Marcell Stippinger, Zsigmond Benkő, Ádám Zlatniczky, Attila Bencze, Kinga Moldován, Katalin Szádeczky-Kardoss, Sándor Borbély, Ildikó Világi, András Telcs
Publikováno v:
IBRO Neuroscience Reports, Vol 15, Iss , Pp S800- (2023)
Externí odkaz:
https://doaj.org/article/cc6f49d22aff422ca160c9204e0e535d
Autor:
Marcell Stippinger, Dávid Hanák, Marcell T. Kurbucz, Gergely Hanczár, Olivér M. Törteli, Zoltán Somogyvári
Publikováno v:
SoftwareX, Vol 22, Iss , Pp 101366- (2023)
The lack of freely available (real-life or synthetic) high or ultra-high dimensional, multi-class datasets may hamper the rapidly growing research on feature screening, especially in the field of biometrics, where the usage of such datasets is common
Externí odkaz:
https://doaj.org/article/b19cb7d431a24436bb5c11452a33216e
Autor:
Zsigmond Benkő, Marcell Stippinger, Roberta Rehus, Attila Bencze, Dániel Fabó, Boglárka Hajnal, Loránd G. Eröss, András Telcs, Zoltán Somogyvári
Publikováno v:
PeerJ Computer Science, Vol 8, p e790 (2022)
Data dimensionality informs us about data complexity and sets limit on the structure of successful signal processing pipelines. In this work we revisit and improve the manifold adaptive Farahmand-Szepesvári-Audibert (FSA) dimension estimator, making
Externí odkaz:
https://doaj.org/article/7e962ab5b28c42f0a3c14b69bfc8eb13
Publikováno v:
Mathematics, Vol 11, Iss 4, p 852 (2023)
Our proposed method for exploring the causal discovery of stochastic dynamic systems is designed to overcome the limitations of existing methods in detecting hidden and common drivers. The method is based on a simple principle and is presented in a n
Externí odkaz:
https://doaj.org/article/15f44dcee7cb401ebc98bcaba26de0ff
Autor:
Gergely Hanczár, Marcell Stippinger, Dávid Hanák, Marcell T Kurbucz, Olivér M Törteli, Ágnes Chripkó, Zoltán Somogyvári
Publikováno v:
Machine Learning: Science and Technology, Vol 4, Iss 4, p 045012 (2023)
In recent years, several screening methods have been published for ultrahigh-dimensional data that contain hundreds of thousands of features, many of which are irrelevant or redundant. However, most of these methods cannot handle data with thousands
Externí odkaz:
https://doaj.org/article/b9e76c4766c04692a96738a53f464413
Publikováno v:
Entropy, Vol 23, Iss 11, p 1450 (2021)
This work is about observational causal discovery for deterministic and stochastic dynamic systems. We explore what additional knowledge can be gained by the usage of standard conditional independence tests and if the interacting systems are located
Externí odkaz:
https://doaj.org/article/3016516300fe43c8bdcc2fe697811fac
Autor:
Marcell Stippinger, Dávid Hanák, Marcell T. Kurbucz, Gergely Hanczár, Olivér M. Törteli, Zoltán Somogyvári
The lack of freely available (real-life or synthetic) high or ultra-high dimensional, multi-class datasets may hamper the rapidly growing research on feature screening, especially in the field of biometrics, where the usage of such datasets is common
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::841e1d08533621d44a3119ce313cf5d4
http://arxiv.org/abs/2206.10747
http://arxiv.org/abs/2206.10747
Autor:
Bálint Varga, Marcell Stippinger, Zsigmond Benkő, Dániel Fabó, Péter Halász, Loránd Erőss, Zoltán Somogyvári, András Telcs
Publikováno v:
SSRN Electronic Journal.
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::467e6c14903efc480d0c220a0898c74a
https://doi.org/10.2139/ssrn.4294786
https://doi.org/10.2139/ssrn.4294786
Publikováno v:
Entropy, Vol 23, Iss 1450, p 1450 (2021)
Entropy
Volume 23
Issue 11
Entropy
Volume 23
Issue 11
This work is about observational causal discovery for deterministic and stochastic dynamic systems. We explore what additional knowledge can be gained by the usage of standard conditional independence tests and if the interacting systems are located
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::652b3c575d0ae733902cb713896cbef7
https://www.mdpi.com/1099-4300/23/11/1450
https://www.mdpi.com/1099-4300/23/11/1450