Tekijä: Alexandru Aleman; Haakan Hedenmalm; Dmitry Khavinson; Mihai Putinar Kustantaja: Springer Nature Switzerland AG (2019) Saatavuus: Noin 16-19 arkipäivää
Tekijä: Alexandru Aleman; Haakan Hedenmalm; Dmitry Khavinson; Mihai Putinar Kustantaja: Springer Nature Switzerland AG (2020) Saatavuus: Noin 16-19 arkipäivää
Tekijä: Ernst Albrecht (ed.); Raúl Curto (ed.); Michael Hartz (ed.); Mihai Putinar (ed.) Kustantaja: Birkhäuser (2023) Saatavuus: Noin 16-19 arkipäivää
Cambridge University Press Sivumäärä: 188 sivua Asu: Kovakantinen kirja Painos: New edition Julkaisuvuosi: 2022, 07.04.2022 (lisätietoa) Kieli: Englanti
The Christoffel–Darboux kernel, a central object in approximation theory, is shown to have many potential uses in modern data analysis, including applications in machine learning. This is the first book to offer a rapid introduction to the subject, illustrating the surprising effectiveness of a simple tool. Bridging the gap between classical mathematics and current evolving research, the authors present the topic in detail and follow a heuristic, example-based approach, assuming only a basic background in functional analysis, probability and some elementary notions of algebraic geometry. They cover new results in both pure and applied mathematics and introduce techniques that have a wide range of potential impacts on modern quantitative and qualitative science. Comprehensive notes provide historical background, discuss advanced concepts and give detailed bibliographical references. Researchers and graduate students in mathematics, statistics, engineering or economics will find new perspectives on traditional themes, along with challenging open problems.
