学习非参数的理论基础，非常新的教材


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.


Product details
Publisher ‏ : ‎ Birkhäuser; 1st ed. 2022 edition (May 18, 2022)
Language ‏ : ‎ English
Paperback ‏ : ‎ 166 pages