東華大學圖書館 |

語系: 繁體中文

說明(常見問題)

回圖書館首頁

手機版館藏查詢

登入

回首頁 到查詢結果 [ author_sort:"suzuki, joe." ]

切換: 標籤 | MARC模式 | ISBD

Kernel methods for machine learning ...

Suzuki, Joe.

FindBook

Google Book

Amazon

博客來

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

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Kernel methods for machine learning with Math and R/ by Joe Suzuki.
其他題名:	100 exercises for building logic /
作者:	Suzuki, Joe.
出版者:	Singapore :Springer Nature Singapore : : 2022.,
面頁冊數:	xii, 196 p. :ill., digital ;24 cm.
內容註:	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
標題:	Machine learning - Mathematics. -
電子資源:	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 /
LDR:02473nmm a2200325 a 4500 001 2299899
003 DE-He213
005 20220503235344.0
006 m d
007 cr nn 008maaau
008 230324s2022 si s 0 eng d
020 $a 9789811903984 $q (electronic bk.)
020 $a 9789811903977 $q (paper)
024 7 $a 10.1007/978-981-19-0398-4 $2 doi
035 $a 978-981-19-0398-4
040 $a GP $c GP
041 0 $a eng
050 4 $a Q325.5
072 7 $a UYQ $2 bicssc
072 7 $a COM004000 $2 bisacsh
072 7 $a UYQ $2 thema
082 0 4 $a 006.31 $2 23
090 $a Q325.5 $b .S968 2022
100 1 $a Suzuki, Joe. $3 2165769
245 1 0 $a Kernel methods for machine learning with Math and R $h [electronic resource] : $b 100 exercises for building logic / $c by Joe Suzuki.
260 $a Singapore : $b Springer Nature Singapore : $b Imprint: Springer, $c 2022.
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
773 0 $t Springer Nature eBook
856 4 0 $u https://doi.org/10.1007/978-981-19-0398-4
950 $a Computer Science (SpringerNature-11645)