- Standort
-
Deutsche Nationalbibliothek Frankfurt am Main
- Umfang
-
Online-Ressource
- Sprache
-
Englisch
- Anmerkungen
-
Dortmund, Technische Universität, Dissertation, 2021
- Klassifikation
-
Statistik
- Schlagwort
-
Merkmalsextraktion
Klassifikation
Hochdimensionale Daten
- Ereignis
-
Veröffentlichung
- (wo)
-
Dortmund
- (wer)
-
Universitätsbibliothek Dortmund
- (wann)
-
2020
- Urheber
- Beteiligte Personen und Organisationen
-
Rahnenführer, Jörg
Weihs, Claus
- Handle
-
2003/40023
- URN
-
urn:nbn:de:101:1-2021021202421868468918
- Rechteinformation
-
Der Zugriff auf das Objekt ist unbeschränkt möglich.
- Letzte Aktualisierung
-
15.08.2025, 07:26 MESZ
Datenpartner
Dieses Objekt wird bereitgestellt von:
Deutsche Nationalbibliothek. Bei Fragen zum Objekt wenden Sie sich bitte an den Datenpartner.
Deutsche Nationalbibliothek. Bei Fragen zum Objekt wenden Sie sich bitte an den Datenpartner.
Beteiligte
- Bommert, Andrea Martina
- Rahnenführer, Jörg
- Weihs, Claus
- Universitätsbibliothek Dortmund
Entstanden
- 2020
Ähnliche Objekte (12)
Variables selection in observational and experimental studies
Local Modelling in Classification on Different Feature Subspaces
Improving supervised music classification by means of multi-objective evolutionary feature selection
D-optimal plans for variable selection in data bases
Stability of multivariate representation of business cycles over time
Fitting simulation input models for correlated traffic data
Combining mental fit and data fit for classification rule selection
Variable selection for discrimination of more than two classes where data are sparse
Improving Feature Extraction by Replacing the Fisher Criterion by an Upper Error Bound
Experimental Design for Variable Selection in data bases
Model and Algorithm Selection in Statistical Learning and Optimization.
Clustering techniques for the detection of business cycles
Variables selection in observational and experimental studies
Local Modelling in Classification on Different Feature Subspaces
Improving supervised music classification by means of multi-objective evolutionary feature selection
D-optimal plans for variable selection in data bases
Stability of multivariate representation of business cycles over time
Fitting simulation input models for correlated traffic data
Combining mental fit and data fit for classification rule selection
Variable selection for discrimination of more than two classes where data are sparse
Improving Feature Extraction by Replacing the Fisher Criterion by an Upper Error Bound
Experimental Design for Variable Selection in data bases
Model and Algorithm Selection in Statistical Learning and Optimization.
Clustering techniques for the detection of business cycles
Variables selection in observational and experimental studies
Local Modelling in Classification on Different Feature Subspaces
Improving supervised music classification by means of multi-objective evolutionary feature selection
D-optimal plans for variable selection in data bases
Stability of multivariate representation of business cycles over time
Fitting simulation input models for correlated traffic data
Combining mental fit and data fit for classification rule selection
Variable selection for discrimination of more than two classes where data are sparse
Improving Feature Extraction by Replacing the Fisher Criterion by an Upper Error Bound
Experimental Design for Variable Selection in data bases
Model and Algorithm Selection in Statistical Learning and Optimization.