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

切換: 標籤 | MARC模式 | ISBD

Random matrix methods for machine le...

Couillet, Romain.

FindBook

Google Book

Amazon

博客來

Random matrix methods for machine learning

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Random matrix methods for machine learning/ Romain Couillet, Grenoble Alpes University, Zhenyu Liao, Huazhong University of Science and Technology.
作者:	Couillet, Romain.
其他作者:	Liao, Zhenyu.
出版者:	Cambridge, United Kingdom ; New York, NY, USA :Cambridge University Press, : 2022.,
面頁冊數:	vi, 402 p. :ill., digital ;25 cm.
附註:	Title from publisher's bibliographic system (viewed on 30 Jun 2022).
標題:	Machine learning - Mathematics. -
電子資源:	https://doi.org/10.1017/9781009128490
ISBN:	9781009128490

Random matrix methods for machine learning
Couillet, Romain.

Random matrix methods for machine learning[electronic resource] /Romain Couillet, Grenoble Alpes University, Zhenyu Liao, Huazhong University of Science and Technology. - Cambridge, United Kingdom ; New York, NY, USA :Cambridge University Press,2022. - vi, 402 p. :ill., digital ;25 cm.

Title from publisher's bibliographic system (viewed on 30 Jun 2022).

This book presents a unified theory of random matrices for applications in machine learning, offering a large-dimensional data vision that exploits concentration and universality phenomena. This enables a precise understanding, and possible improvements, of the core mechanisms at play in real-world machine learning algorithms. The book opens with a thorough introduction to the theoretical basics of random matrices, which serves as a support to a wide scope of applications ranging from SVMs, through semi-supervised learning, unsupervised spectral clustering, and graph methods, to neural networks and deep learning. For each application, the authors discuss small- versus large-dimensional intuitions of the problem, followed by a systematic random matrix analysis of the resulting performance and possible improvements. All concepts, applications, and variations are illustrated numerically on synthetic as well as real-world data, with MATLAB and Python code provided on the accompanying website.

ISBN: 9781009128490Subjects--Topical Terms:

3442737
Machine learning
--Mathematics.

LC Class. No.: Q325.5 / .C69 2022

Dewey Class. No.: 006.31

Random matrix methods for machine learning
LDR:01916nmm a2200241 a 4500 001 2324486
003 UkCbUP
005 20220704121753.0
006 m d
007 cr nn 008maaau
008 231215s2022 enk o 1 0 eng d
020 $a 9781009128490 $q (electronic bk.)
020 $a 9781009123235 $q (hardback)
035 $a CR9781009128490
040 $a UkCbUP $b eng $c UkCbUP $d GP
050 0 0 $a Q325.5 $b .C69 2022
082 0 4 $a 006.31 $2 23
090 $a Q325.5 $b .C854 2022
100 1 $a Couillet, Romain. $3 3645763
245 1 0 $a Random matrix methods for machine learning $h [electronic resource] / $c Romain Couillet, Grenoble Alpes University, Zhenyu Liao, Huazhong University of Science and Technology.
260 $a Cambridge, United Kingdom ; New York, NY, USA : $b Cambridge University Press, $c 2022.
300 $a vi, 402 p. : $b ill., digital ; $c 25 cm.
500 $a Title from publisher's bibliographic system (viewed on 30 Jun 2022).
520 $a This book presents a unified theory of random matrices for applications in machine learning, offering a large-dimensional data vision that exploits concentration and universality phenomena. This enables a precise understanding, and possible improvements, of the core mechanisms at play in real-world machine learning algorithms. The book opens with a thorough introduction to the theoretical basics of random matrices, which serves as a support to a wide scope of applications ranging from SVMs, through semi-supervised learning, unsupervised spectral clustering, and graph methods, to neural networks and deep learning. For each application, the authors discuss small- versus large-dimensional intuitions of the problem, followed by a systematic random matrix analysis of the resulting performance and possible improvements. All concepts, applications, and variations are illustrated numerically on synthetic as well as real-world data, with MATLAB and Python code provided on the accompanying website.
650 0 $a Machine learning $x Mathematics. $3 3442737
650 0 $a Matrix analytic methods. $3 621239
700 1 $a Liao, Zhenyu. $3 3540235
856 4 0 $u https://doi.org/10.1017/9781009128490