- Standort
-
Deutsche Nationalbibliothek Frankfurt am Main
- ISSN
-
1573-0565
- Umfang
-
Online-Ressource
- Sprache
-
Englisch
- Anmerkungen
-
online resource.
- Erschienen in
-
High-dimensional Bayesian optimization using low-dimensional feature spaces ; volume:109 ; number:9-10 ; day:21 ; month:9 ; year:2020 ; pages:1925-1943 ; date:9.2020
Machine learning ; 109, Heft 9-10 (21.9.2020), 1925-1943, 9.2020
- Urheber
-
Moriconi, Riccardo
Deisenroth, Marc Peter
Sesh Kumar, K. S.
- Beteiligte Personen und Organisationen
-
SpringerLink (Online service)
- DOI
-
10.1007/s10994-020-05899-z
- URN
-
urn:nbn:de:101:1-2020111622374480709703
- Rechteinformation
-
Open Access; Der Zugriff auf das Objekt ist unbeschränkt möglich.
- Letzte Aktualisierung
-
15.08.2025, 07:25 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
- Moriconi, Riccardo
- Deisenroth, Marc Peter
- Sesh Kumar, K. S.
- SpringerLink (Online service)
Ähnliche Objekte (12)
High-dimensional variable selection via low-dimensional adaptive learning
Remarks on Low-Dimensional Projections of High-Dimensional Distributions
Understanding high-dimensional spaces
From low-dimensional model selection to high-dimensional inference: tailoring Bayesian methods to biological dynamical systems
Visual analysis of high-dimensional spaces
Visual Analysis of High-Dimensional Spaces
Reducibility of low-dimensional Poincaré duality spaces
Decomposing reach set computations with low-dimensional sets and high-dimensional matrices (extended version)
Detecting and approximating decision boundaries in low-dimensional spaces
Fast Nearest Neighbor Search in High-Dimensional Spaces
Prototype based clustering in high-dimensional feature spaces
Proximal nested sampling for high-dimensional Bayesian model selection
High-dimensional variable selection via low-dimensional adaptive learning
Remarks on Low-Dimensional Projections of High-Dimensional Distributions
Understanding high-dimensional spaces
From low-dimensional model selection to high-dimensional inference: tailoring Bayesian methods to biological dynamical systems
Visual analysis of high-dimensional spaces
Visual Analysis of High-Dimensional Spaces
Reducibility of low-dimensional Poincaré duality spaces
Decomposing reach set computations with low-dimensional sets and high-dimensional matrices (extended version)
Detecting and approximating decision boundaries in low-dimensional spaces
Fast Nearest Neighbor Search in High-Dimensional Spaces
Prototype based clustering in high-dimensional feature spaces
Proximal nested sampling for high-dimensional Bayesian model selection
High-dimensional variable selection via low-dimensional adaptive learning
Remarks on Low-Dimensional Projections of High-Dimensional Distributions
Understanding high-dimensional spaces
From low-dimensional model selection to high-dimensional inference: tailoring Bayesian methods to biological dynamical systems
Visual analysis of high-dimensional spaces
Visual Analysis of High-Dimensional Spaces
Reducibility of low-dimensional Poincaré duality spaces
Decomposing reach set computations with low-dimensional sets and high-dimensional matrices (extended version)
Detecting and approximating decision boundaries in low-dimensional spaces
Fast Nearest Neighbor Search in High-Dimensional Spaces
Prototype based clustering in high-dimensional feature spaces