Tikhonov regularization with oversmoothing penalty for linear statistical inverse learning problems
Küçük Resim Yok
Tarih
2019
Yazarlar
Dergi Başlığı
Dergi ISSN
Cilt Başlığı
Yayıncı
Maltepe Üniversitesi
Erişim Hakkı
CC0 1.0 Universal
info:eu-repo/semantics/openAccess
info:eu-repo/semantics/openAccess
Özet
In this paper, we consider the linear ill-posed inverse problem with noisy data in the statistical learning setting. The Tikhonov regularization scheme in Hilbert scales is considered in the reproducing kernel Hilbert space framework to reconstruct the estimator from the random noisy data. We discuss the rates of convergence for the regularized solution under the prior assumptions and link condition. For regression functions with smoothness given in terms of source conditions the error bound can explicitly be established.
Açıklama
Anahtar Kelimeler
Statistical inverse problem, Tikhonov regularization, Hilbert Scales, Reproducing kernel Hilbert space, Minimax convergence rates
Kaynak
Third International Conference of Mathematical Sciences
WoS Q Değeri
Scopus Q Değeri
Cilt
Sayı
Künye
Rastogi, A. (2019). Tikhonov regularization with oversmoothing penalty for linear statistical inverse learning problems. Third International Conference of Mathematical Sciences, Maltepe Üniversitesi. s. 1-4.
