Tuning structure learning algorithms with out-of-sample and resampling strategies
- Location
-
Deutsche Nationalbibliothek Frankfurt am Main
- Extent
-
1 Online-Ressource.
- Language
-
Englisch
- Bibliographic citation
-
Tuning structure learning algorithms with out-of-sample and resampling strategies ; volume:66 ; number:8 ; day:24 ; month:5 ; year:2024 ; pages:4927-4955 ; date:8.2024
Knowledge and information systems ; 66, Heft 8 (24.5.2024), 4927-4955, 8.2024
- Creator
-
Chobtham, Kiattikun
Constantinou, Anthony C.
- Contributor
-
SpringerLink (Online service)
- DOI
-
10.1007/s10115-024-02111-9
- URN
-
urn:nbn:de:101:1-2410080809396.388595685415
- Rights
-
Open Access; Der Zugriff auf das Objekt ist unbeschränkt möglich.
- Last update
-
15.08.2025, 7:24 AM CEST
Data provider
This object is provided by:
Deutsche Nationalbibliothek. If you have any questions about the object, please contact the data provider.
Deutsche Nationalbibliothek. If you have any questions about the object, please contact the data provider.
Associated
- Chobtham, Kiattikun
- Constantinou, Anthony C.
- SpringerLink (Online service)
Other Objects (12)
Resampling-based tuning of ordered model selection
Improving network inference algorithms using resampling methods
Resampling algorithms for high energy physics simulations
Tuning Evolutionary Algorithms
Jahresabschlussprüfung und Resampling-Verfahren
The comparison of automated clustering algorithms for resampling representative conformer ensembles with RMSD matrix
Resampling methods for dependent data
Improved Particle Filter Resampling Architectures
Portfolio Resampling and Efficiency Issues
Extremal index: estimation and resampling
Rank-invariant resampling based estimation of false discovery rate for analysis of small sample microarray data
Testing Out-of-sample Portfolio Performance
Resampling-based tuning of ordered model selection
Improving network inference algorithms using resampling methods
Resampling algorithms for high energy physics simulations
Tuning Evolutionary Algorithms
Jahresabschlussprüfung und Resampling-Verfahren
The comparison of automated clustering algorithms for resampling representative conformer ensembles with RMSD matrix
Resampling methods for dependent data
Improved Particle Filter Resampling Architectures
Portfolio Resampling and Efficiency Issues
Extremal index: estimation and resampling
Rank-invariant resampling based estimation of false discovery rate for analysis of small sample microarray data
Testing Out-of-sample Portfolio Performance
Resampling-based tuning of ordered model selection
Improving network inference algorithms using resampling methods
Resampling algorithms for high energy physics simulations
Tuning Evolutionary Algorithms
Jahresabschlussprüfung und Resampling-Verfahren
The comparison of automated clustering algorithms for resampling representative conformer ensembles with RMSD matrix
Resampling methods for dependent data
Improved Particle Filter Resampling Architectures
Portfolio Resampling and Efficiency Issues
Extremal index: estimation and resampling
Rank-invariant resampling based estimation of false discovery rate for analysis of small sample microarray data