Kernel Mode Decomposition and the Programming of Kernels / Houman Owhadi, Clint Scovel, Gene Ryan Yoo

• Autore: Owhadi, Houman
• Pubblicazione: Cham, : Springer, 2021
• Descrizione fisica: x, 118 p. : ill. ; 24 cm
• Soggetto topico: 68T10 - Pattern recognition, speech recognition [MSC 2020]; 62J02 - General nonlinear regression [MSC 2020]; 62-XX - Statistics [MSC 2020]; 62G07 - Density estimation [MSC 2020]; 62J12 - Generalized linear models (logistic models) [MSC 2020]; 62R07 - Statistical aspects of big data and data science [MSC 2020]; 62H30 - Classification and discrimination; cluster analysis (statistical aspects) [MSC 2020]; 62G08 - Nonparametric regression and quantile regression [MSC 2020]; 68T09 - Computational aspects of data analysis and big data [MSC 2020]
• Soggetto non controllato: Additive models; Empirical mode decomposition; Gaussian process regression; Kernel methods; Time-frequency decomposition
• Altri autori: Scovel, Clint Yoo, Gene Ryan
• Titolo autorizzato: Kernel Mode Decomposition and the Programming of Kernels
• Formato: Materiale a stampa
• Livello bibliografico: Monografia
• Lingua di pubblicazione: Inglese
• Record Nr.: VAN0274859
• Lo trovi qui: Univ. Vanvitelli
• Localizzazioni e accesso elettronico: https://doi.org/10.1007/978-3-030-82171-5
• Serie: Surveys and Tutorials in the Applied Mathematical Sciences Berlin [etc.] . -Springer ; 8