EAN: 9783030983154

COMPACT TEXTBOOKS IN MATHEMATICS AN INTRODUCT...

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.

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