La politique culturelle au Kenia

• Record Nr.: UNINA990003762060403321
• Autore: Ndeti, Kivuto
• Pubbl/distr/stampa: Paris : Les Presses de l'Unesco, 1975
• Descrizione fisica: 76 p. ; 24 cm
• Locazione: BFS
• Collocazione: 19/100
• Lingua di pubblicazione: Italiano
• Formato: Materiale a stampa
• Livello bibliografico: Monografia

Regularization theory for ill-posed problems : selected topics / / by Shuai Lu, Sergei V. Pereverzev

• Record Nr.: UNINA9910787646303321
• Autore: Lu Shuai <1976->
• Pubbl/distr/stampa: Berlin ; ; Boston : , : Walter de Gruyter, , [2013]; ©2013
• ISBN: 3-11-028649-1
• Descrizione fisica: 1 online resource (304 p.)
• Collana: Inverse and ill-posed problems series ; ; 58
• Altri autori (Persone): PereverzevSergei V
• Disciplina: 518/.53
• Soggetti: Numerical analysis - Improperly posed problems; Numerical differentiation
• Lingua di pubblicazione: Inglese
• Formato: Materiale a stampa
• Livello bibliografico: Monografia
• Note generali: Description based upon print version of record.
• Nota di bibliografia: Includes bibliographical references and index.
• Nota di contenuto: Front matter -- Preface -- Contents -- Chapter 1. An introduction using classical examples -- Chapter 2. Basics of single parameter regularization schemes -- Chapter 3. Multiparameter regularization -- Chapter 4. Regularization algorithms in learning theory -- Chapter 5. Meta-learning approach to regularization - case study: blood glucose ; prediction -- Bibliography -- Index
• Sommario/riassunto: This monograph is a valuable contribution to the highly topical and extremely productive field of regularization methods for inverse and ill-posed problems. The author is an internationally outstanding and accepted mathematician in this field. In his book he offers a well-balanced mixture of basic and innovative aspects. He demonstrates new, differentiated viewpoints, and important examples for applications. The book demonstrates the current developments in the field of regularization theory, such as multi parameter regularization and regularization in learning theory. The book is written for graduate and PhDs