
An Introduction to Artificial Intelligence Based on Reproducing Kernel Hilbert Spaces
Description
Alles über E-Books | Antworten auf Fragen rund um E-Books, Kopierschutz und Dateiformate finden Sie in unserem Info- & Hilfebereich.
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. The author demonstrates how the concept of reproducing kernel Hilbert Spaces (RKHS), accompanied with tools from regularization theory, can be effectively used in the design and justification of kernel learning algorithms, which can address problems in several areas of artificial intelligence. Also provided is a detailed description of two biomedical applications of the considered algorithms, demonstrating how close the theory is to being practically implemented.
Among the book's several unique features is its analysis of a large class of algorithms of the Learning Theory that essentially comprise every linear regularization scheme, including Tikhonov regularization as a specific case. It also provides a methodology for analyzing not only different supervised learning problems, such as regression or ranking, but also different learning scenarios, such as unsupervised domain adaptation or reinforcement learning. By analyzing these topics using the same theoretical framework, rather than approaching them separately, their presentation is streamlined and made more approachable.
An Introduction to Artificial Intelligence Based on Reproducing Kernel Hilbert Spaces is an ideal resource for graduate and postgraduate courses in computational mathematics and data science.
Reviews / Votes
"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)More details
Other editions
Additional editions

Person
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).
Content
Introduction.- Learning in Reproducing Kernel Hilbert Spaces and related integral operators.- Selected topics of the regularization theory.- Regularized learning in RKHS.- Examples of Applications.
System requirements
File format: PDF
Copy protection: Watermark-DRM (Digital Rights Management)
System requirements:
- Computer (Windows; MacOS X; Linux): Use the free software Adobe Reader, Adobe Digital Editions, or any other PDF viewer of your choice (see eBook Help).
- Tablet/Smartphone (Android; iOS): Install the free app Adobe Digital Editions or another reading app for eBooks, e.g., PocketBook (see eBook Help).
- E-reader: Bookeen, Kobo, Pocketbook, Sony, Tolino and many more (only limited: Kindle).
The file format PDF always displays a book page identically on any hardware. This makes PDF suitable for complex layouts such as those used in textbooks and reference books (images, tables, columns, footnotes). Unfortunately, on the small screens of e-readers or smartphones, PDFs are rather annoying, requiring too much scrolling.
This eBook uses Watermark-DRM, a „soft” copy protection. This means that there are no technical restrictions to prevent illegal distribution. However, there is a personalised watermark embedded in the eBook that can be used to identify the purchaser of the eBook in the event of misuse and to provide evidence for legal purposes.
For more information, see our eBook Help page.