Introduction to Artificial Intelligence Based on Reproducing Kernel Hilbert Spaces

Häftad, Engelska, 2022

Del i serien Compact Textbooks in Mathematics

536 kr

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Beskrivning

This textbook provides an in-depth exploration of statistical learning with reproducing kernels, an active area of research that can shed light on trends associated with deep neural networks.

Produktinformation

Utgivningsdatum:2022-05-18
Mått:155 x 235 x 9 mm
Vikt:296 g
Format:Häftad
Språk:Engelska
Serie:Compact Textbooks in Mathematics
Antal sidor:152
Förlag:Springer Nature Switzerland AG
ISBN:9783030983154

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Mer om författaren

Sergei V. Pereverzyev is Professor and Senior Fellow of the Johann Radon Institute for Computational and Applied Mathematics, Austrian Academy of Sciences. He is the second recipient of the International Prize for Achievement in Information-Based Complexity (2000), and the inventor of a patented innovation in diabetes technology (2019). He is also the author of two monographs, over one hundred scholarly articles and serves as a member of editorial boards of such international journals as Applied and Computational Harmonic Analysis, Journal of Complexity, Computational Methods in Applied Mathematics, International Journal on Geomathematics, Journal of Diabetes & Metabolism, Frontiers in Applied Mathematics and Statistics, International Journal of Wavelets, Multiresolution and Information Processing. He was principal investigator and person in charge of several research projects granted by research programs FP7 and Horizon-2020, the Austrian Science Fund (FWF), the Austrian Research Promotion Agency (FFG) and the German Research Foundation (DFG).

Recensioner i media

“This is a very beautiful book … . Everyone with mathematical background and interested in learning theory and regularization should, or rather must, read this book.” (Andreas Wichert, zbMATH 1500.68004, 2023)

Innehållsförteckning

Introduction.- Learning in Reproducing Kernel Hilbert Spaces and related integral operators.- Selected topics of the regularization theory.- Regularized learning in RKHS.- Examples of Applications.