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Kernel methods for machine learning ...

Suzuki, Joe.

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

Record Type:	Electronic resources : Monograph/item
Title/Author:	Kernel methods for machine learning with Math and R/ by Joe Suzuki.
Reminder of title:	100 exercises for building logic /
Author:	Suzuki, Joe.
Published:	Singapore :Springer Nature Singapore : : 2022.,
Description:	xii, 196 p. :ill., digital ;24 cm.
[NT 15003449]:	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.
Contained By:	Springer Nature eBook
Subject:	Machine learning - Mathematics. -
Online resource:	https://doi.org/10.1007/978-981-19-0398-4
ISBN:	9789811903984

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

Kernel methods for machine learning with Math and R100 exercises for building logic /[electronic resource] :by Joe Suzuki. - Singapore :Springer Nature Singapore :2022. - xii, 196 p. :ill., digital ;24 cm.

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 book's 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: 9789811903984

Standard No.: 10.1007/978-981-19-0398-4doiSubjects--Topical Terms:

3442737
Machine learning
--Mathematics.

LC Class. No.: Q325.5

Dewey Class. No.: 006.31

Kernel methods for machine learning with Math and R = 100 exercises for building logic /
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300 $a xii, 196 p. : $b ill., digital ; $c 24 cm.
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 book's 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 Machine learning $x Mathematics. $3 3442737
650 0 $a Kernel functions. $3 562986
650 0 $a R (Computer program language) $3 784593
650 1 4 $a Artificial Intelligence. $3 769149
650 2 4 $a Machine Learning. $3 3382522
650 2 4 $a Statistical Learning. $3 3597795
650 2 4 $a Data Science. $3 3538937
650 2 4 $a Computational Intelligence. $3 1001631
710 2 $a SpringerLink (Online service) $3 836513
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856 4 0 $u https://doi.org/10.1007/978-981-19-0398-4
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