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Kernel methods for machine learning with Math and R :100 exercises for building logic /

紀錄類型:	書目-語言資料,印刷品 : 單行本
正題名/作者:	Kernel methods for machine learning with Math and R :/ Joe Suzuki.
其他題名:	100 exercises for building logic /
作者:	Suzuki, Joe.
出版者:	Singapore :Springer,2022.
面頁冊數:	xii, 196 p. :ill. (some col.) ;24 cm.
標題:	Kernel functions. -
ISBN:	9789811903977
ISBN:	9811903972

Kernel methods for machine learning with Math and R :100 exercises for building logic /
Suzuki, Joe.

Kernel methods for machine learning with Math and R :100 exercises for building logic /Joe Suzuki. - Singapore :Springer,2022. - xii, 196 p. :ill. (some col.) ;24 cm.

Includes bibliographical references.

Chapter 1: Positive Definite Kernels -- Chapter 2: Hilbert Spaces -- Chapter 3: Reproducing Kernel Hilbert Space -- Chapter 4: Kernel Computations -- Chapter 5: MMD and HSIC -- Chapter 6: Gaussian Processes and Functional Data Analyses.

The most crucial ability for machine learning and data science is mathematical logic for grasping their essence rather than relying on knowledge or experience. This textbook addresses the fundamentals of kernel methods for machine learning by considering relevant math problems and building R programs. The books main features are as follows: The content is written in an easy-to-follow and self-contained style. The book includes 100 exercises, which have been carefully selected and refined. As their solutions are provided in the main text, readers can solve all of the exercises by reading the book. The mathematical premises of kernels are proven and the correct conclusions are provided, helping readers to understand the nature of kernels. Source programs and running examples are presented to help readers acquire a deeper understanding of the mathematics used. Once readers have a basic understanding of the functional analysis topics covered in Chapter 2, the applications are discussed in the subsequent chapters. Here, no prior knowledge of mathematics is assumed. This book considers both the kernel for reproducing kernel Hilbert space (RKHS) and the kernel for the Gaussian process; a clear distinction is made between the two.

ISBN: 9789811903977NT1416Subjects--Topical Terms:

166469
Kernel functions.

LC Class. No.: Q325.5

Dewey Class. No.: 006.3/1

Kernel methods for machine learning with Math and R :100 exercises for building logic /
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245 1 0 $a Kernel methods for machine learning with Math and R : $b 100 exercises for building logic / $c Joe Suzuki.
260 $a Singapore : $b Springer, $c 2022.
300 $a xii, 196 p. : $b ill. (some col.) ; $c 24 cm.
504 $a Includes bibliographical references.
505 0 $a Chapter 1: Positive Definite Kernels -- Chapter 2: Hilbert Spaces -- Chapter 3: Reproducing Kernel Hilbert Space -- Chapter 4: Kernel Computations -- Chapter 5: MMD and HSIC -- Chapter 6: Gaussian Processes and Functional Data Analyses.
520 $a The most crucial ability for machine learning and data science is mathematical logic for grasping their essence rather than relying on knowledge or experience. This textbook addresses the fundamentals of kernel methods for machine learning by considering relevant math problems and building R programs. The books main features are as follows: The content is written in an easy-to-follow and self-contained style. The book includes 100 exercises, which have been carefully selected and refined. As their solutions are provided in the main text, readers can solve all of the exercises by reading the book. The mathematical premises of kernels are proven and the correct conclusions are provided, helping readers to understand the nature of kernels. Source programs and running examples are presented to help readers acquire a deeper understanding of the mathematics used. Once readers have a basic understanding of the functional analysis topics covered in Chapter 2, the applications are discussed in the subsequent chapters. Here, no prior knowledge of mathematics is assumed. This book considers both the kernel for reproducing kernel Hilbert space (RKHS) and the kernel for the Gaussian process; a clear distinction is made between the two.
650 0 $a Kernel functions. $3 166469
650 0 $a R (Computer program language) $3 177289
650 0 $a Machine learning $x Mathematics. $3 1000128497

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