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Titlebook: An Introduction to Artificial Intelligence Based on Reproducing Kernel Hilbert Spaces; Sergei Pereverzyev Textbook 2022 The Editor(s) (if

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发表于 2025-3-21 17:49:34 | 显示全部楼层 |阅读模式
期刊全称An Introduction to Artificial Intelligence Based on Reproducing Kernel Hilbert Spaces
影响因子2023Sergei Pereverzyev
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发行地址Explores statistical learning with reproducing kernels, offering insight on trends associated with deep neural networks.Analyzes a class of algorithms of the Learning Theory, comprising most linear re
学科分类Compact Textbooks in Mathematics
图书封面Titlebook: An Introduction to Artificial Intelligence Based on Reproducing Kernel Hilbert Spaces;  Sergei Pereverzyev Textbook 2022 The Editor(s) (if
影响因子.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 appro
Pindex Textbook 2022
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,Zusammenfassende Schlußbetrachtung,tive, self-adjoint, and compact operators acting in a Hilbert space .. Therefore, when discussing the general regularization tools for dealing with the ill-posedness, we shall focus on the equations with such operators.
发表于 2025-3-22 12:05:36 | 显示全部楼层
https://doi.org/10.1007/978-3-030-98316-1Reproducing kernel Hilbert spaces; RKHS; RKHS artificial intelligence; RKHS deep learning; RKHS deep neu
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978-3-030-98315-4The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerl
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https://doi.org/10.1007/978-3-658-04950-8a. Admitting that the above data are usually incomplete, one, however, can try to uncover the relationship between a dependent variable . and an independent variable . by assuming all complicated affecting factors to be random and then employing a technique known as “supervised learning.” In this te
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Selected Topics of the Regularization Theory,tive, self-adjoint, and compact operators acting in a Hilbert space .. Therefore, when discussing the general regularization tools for dealing with the ill-posedness, we shall focus on the equations with such operators.
发表于 2025-3-23 07:28:46 | 显示全部楼层
Sergei PereverzyevExplores statistical learning with reproducing kernels, offering insight on trends associated with deep neural networks.Analyzes a class of algorithms of the Learning Theory, comprising most linear re
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