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The Christoffeldarboux Kernel For Data Analysis Cambridge Monographs On Applied And Computational Mathematics New Jean Bernard Lasserre

Name: The Christoffeldarboux Kernel For Data Analysis Cambridge Monographs On Applied And Computational Mathematics New Jean Bernard Lasserre
SKU: BELL-51992566
Price: 31.00 USD
Availability: InStock
Rating: 4.25 (88 reviews)

SKU: BELL-51992566

The Christoffeldarboux Kernel For Data Analysis Cambridge Monographs On Applied And Computational Mathematics New Jean Bernard Lasserre

$ 31.00 ~~$ 45.00~~ (-31%)

4.3

88 reviews

The Christoffeldarboux Kernel For Data Analysis Cambridge Monographs On Applied And Computational Mathematics New Jean Bernard Lasserre instant download after payment.

Publisher: Cambridge University Press

File Extension: PDF

File size: 5.19 MB

Pages: 188

Author: Jean Bernard Lasserre, Edouard Pauwels, Mihai Putinar

ISBN: 9781108838061, 1108838065

Language: English

Year: 2022

Edition: New

Product desciption

The Christoffeldarboux Kernel For Data Analysis Cambridge Monographs On Applied And Computational Mathematics New Jean Bernard Lasserre by Jean Bernard Lasserre, Edouard Pauwels, Mihai Putinar 9781108838061, 1108838065 instant download after payment.

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.