portada An Introduction to Artificial Intelligence Based on Reproducing Kernel Hilbert Spaces (en Inglés)

An Introduction to Artificial Intelligence Based on Reproducing Kernel Hilbert Spaces (en Inglés)

Formato

Libro Físico

Autor

Sergei Pereverzyev

Editorial

Birkhauser

Idioma

Inglés

N° páginas

152

Encuadernación

Tapa Blanda

Dimensiones

23.4x15.5x0.8 cm

Peso

0.31 kg.

ISBN13

9783030983154

Categorías

Análisis funcional y transformaciones
Probabilidad y estadística
Inteligencia artificial

An Introduction to Artificial Intelligence Based on Reproducing Kernel Hilbert Spaces (en Inglés)

Sergei Pereverzyev (Autor) · Birkhauser · Tapa Blanda

Libro Nuevo Importado

Envío: 20 a 27 días háb.

$ 108.45$ 54.22

-50%

Costos de importación incluídos en el precio ✅

Libro Nuevo

Quedan más de 100 unidades

$ 54.22

Llega entre el 14 Sep y el 25 Sep a Quito, Pichincha. Seleccionar ubicación

Agregar a lista de deseos

Reseña del libro "An Introduction to Artificial Intelligence Based on Reproducing Kernel Hilbert Spaces (en Inglés)"

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.