語系:
繁體中文
English
說明(常見問題)
圖資館首頁
登入
回首頁
到查詢結果
[ author_sort:"theodoridis, sergios, (1951-)" ]
切換:
標籤
|
MARC模式
|
ISBD
Machine learninga Bayesian and optim...
~
Theodoridis, Sergios, (1951-)
Machine learninga Bayesian and optimization perspective /
紀錄類型:
書目-電子資源 : Monograph/item
正題名/作者:
Machine learningSergios Theodoridis.
其他題名:
a Bayesian and optimization perspective /
作者:
Theodoridis, Sergios,
出版者:
London ;Elsevier :2020.
面頁冊數:
1 online resource (xxvii, 1031 p.) ;illustrations
標題:
Machine learningMathematical models.
電子資源:
https://www.sciencedirect.com/science/book/9780128188033
ISBN:
9780128188040 (electronic bk.)
Machine learninga Bayesian and optimization perspective /
Theodoridis, Sergios,1951-
Machine learning
a Bayesian and optimization perspective /[electronic resource] :Sergios Theodoridis. - 2nd edition. - London ;Elsevier :2020. - 1 online resource (xxvii, 1031 p.) ;illustrations
Includes bibliographical references and index.
This tutorial text gives a unifying perspective on machine learning by covering both probabilistic and deterministic approaches -which are based on optimization techniques - together with the Bayesian inference approach, whose essence lies in the use of a hierarchy of probabilistic models. The book presents the major machine learning methods as they have been developed in different disciplines, such as statistics, statistical and adaptive signal processing and computer science. Focusing on the physical reasoning behind the mathematics, all the various methods and techniques are explained in depth, supported by examples and problems, giving an invaluable resource to the student and researcher for understanding and applying machine learning concepts. The book builds carefully from the basic classical methods to the most recent trends, with chapters written to be as self-contained as possible, making the text suitable for different courses: pattern recognition, statistical/adaptive signal processing, statistical/Bayesian learning, as well as short courses on sparse modeling, deep learning, and probabilistic graphical models. All major classical techniques: Mean/Least-Squares regression and filtering, Kalman filtering, stochastic approximation and online learning, Bayesian classification, decision trees, logistic regression and boosting methods. The latest trends: Sparsity, convex analysis and optimization, online distributed algorithms, learning in RKH spaces, Bayesian inference, graphical and hidden Markov models, particle filtering, deep learning, dictionary learning and latent variables modeling. Case studies - protein folding prediction, optical character recognition, text authorship identification, fMRI data analysis, change point detection, hyperspectral image unmixing, target localization, channel equalization and echo cancellation, show how the theory can be applied. MATLAB code for all the main algorithms are available on an accompanying website, enabling the reader to experiment with the code.
ISBN: 9780128188040 (electronic bk.)Subjects--Topical Terms:
247702
Machine learning
--Mathematical models.Index Terms--Genre/Form:
214472
Electronic books.
LC Class. No.: Q325.5 / .T43 2020
Dewey Class. No.: 006.3/1
Machine learninga Bayesian and optimization perspective /
LDR
:03078cmm a2200277 a 4500
001
601504
006
m o d
007
cr |n|||||||||
008
211110s2020 enka gob 001 0 eng d
020
$a
9780128188040 (electronic bk.)
020
$a
0128188049 (electronic bk.)
020
$a
9780128188033
020
$a
0128188030
035
$a
(OCoLC)1141524576
035
$a
on1141524576
040
$a
YDX
$b
eng
$c
YDX
$d
YDXIT
$d
N
$d
OPELS
$d
OCLCF
$d
UKMGB
$d
UKAHL
$d
WAU
$d
EBLCP
041
0
$a
eng
050
4
$a
Q325.5
$b
.T43 2020
082
0 4
$a
006.3/1
$2
23
100
1
$a
Theodoridis, Sergios,
$d
1951-
$3
214973
245
1 0
$a
Machine learning
$h
[electronic resource] :
$b
a Bayesian and optimization perspective /
$c
Sergios Theodoridis.
250
$a
2nd edition.
260
$a
London ;
$a
San Diego :
$b
Elsevier :
$b
Academic Press,
$c
2020.
300
$a
1 online resource (xxvii, 1031 p.) ;
$b
illustrations
504
$a
Includes bibliographical references and index.
520
$a
This tutorial text gives a unifying perspective on machine learning by covering both probabilistic and deterministic approaches -which are based on optimization techniques - together with the Bayesian inference approach, whose essence lies in the use of a hierarchy of probabilistic models. The book presents the major machine learning methods as they have been developed in different disciplines, such as statistics, statistical and adaptive signal processing and computer science. Focusing on the physical reasoning behind the mathematics, all the various methods and techniques are explained in depth, supported by examples and problems, giving an invaluable resource to the student and researcher for understanding and applying machine learning concepts. The book builds carefully from the basic classical methods to the most recent trends, with chapters written to be as self-contained as possible, making the text suitable for different courses: pattern recognition, statistical/adaptive signal processing, statistical/Bayesian learning, as well as short courses on sparse modeling, deep learning, and probabilistic graphical models. All major classical techniques: Mean/Least-Squares regression and filtering, Kalman filtering, stochastic approximation and online learning, Bayesian classification, decision trees, logistic regression and boosting methods. The latest trends: Sparsity, convex analysis and optimization, online distributed algorithms, learning in RKH spaces, Bayesian inference, graphical and hidden Markov models, particle filtering, deep learning, dictionary learning and latent variables modeling. Case studies - protein folding prediction, optical character recognition, text authorship identification, fMRI data analysis, change point detection, hyperspectral image unmixing, target localization, channel equalization and echo cancellation, show how the theory can be applied. MATLAB code for all the main algorithms are available on an accompanying website, enabling the reader to experiment with the code.
588
$a
Description based on online resource; title from digital title page (viewed on February 28, 2020).
650
0
$a
Machine learning
$x
Mathematical models.
$3
247702
650
0
$a
Bayesian statistical decision theory.
$3
182005
650
0
$a
Mathematical optimization.
$3
183292
655
4
$a
Electronic books.
$2
local.
$3
214472
856
4 0
$u
https://www.sciencedirect.com/science/book/9780128188033
筆 0 讀者評論
全部
電子館藏
館藏
1 筆 • 頁數 1 •
1
條碼號
館藏地
館藏流通類別
資料類型
索書號
使用類型
借閱狀態
預約狀態
備註欄
附件
000000200760
電子館藏
1圖書
電子書
EB Q325.5 .T43 2020 2020
一般使用(Normal)
編目處理中
0
1 筆 • 頁數 1 •
1
多媒體
多媒體檔案
https://www.sciencedirect.com/science/book/9780128188033
評論
新增評論
分享你的心得
Export
取書館別
處理中
...
變更密碼
登入