Benchmark and application of unsupervised classification approaches for univariate data
In the field of nanoscience, clustering methods have gained momentum for the analysis of experimental datasets with the aim of uncovering new physical properties. Here, the authors describe an unsupervised machine learning methodology that selects the optimal combination of feature space, clustering...
Guardado en:
Autores principales: | Maria El Abbassi, Jan Overbeck, Oliver Braun, Michel Calame, Herre S. J. van der Zant, Mickael L. Perrin |
---|---|
Formato: | article |
Lenguaje: | EN |
Publicado: |
Nature Portfolio
2021
|
Materias: | |
Acceso en línea: | https://doaj.org/article/142f18d37da342038eb2d52cf28bd1d9 |
Etiquetas: |
Agregar Etiqueta
Sin Etiquetas, Sea el primero en etiquetar este registro!
|
Ejemplares similares
-
Charge density waves and their transitions in anisotropic quantum Hall systems
por: Yuchi He, et al.
Publicado: (2021) -
Order-of-magnitude differences in computational performance of analog Ising machines induced by the choice of nonlinearity
por: Fabian Böhm, et al.
Publicado: (2021) -
Front-induced transitions control THz waves
por: Aidan W. Schiff-Kearn, et al.
Publicado: (2021) -
Observation of a giant mass enhancement in the ultrafast electron dynamics of a topological semimetal
por: Oliver J. Clark, et al.
Publicado: (2021) -
Thermosuperrepellency of a hot substrate caused by vapour percolation
por: J. Benedikt Schmidt, et al.
Publicado: (2021)