語系:
繁體中文
English
說明(常見問題)
登入
回首頁
切換:
標籤
|
MARC模式
|
ISBD
Computational Intelligence = A Methodological Introduction /
紀錄類型:
書目-語言資料,印刷品 : Monograph/item
正題名/作者:
Computational Intelligence/ by Rudolf Kruse, Sanaz Mostaghim, Christian Borgelt, Christian Braune, Matthias Steinbrecher.
其他題名:
A Methodological Introduction /
作者:
Kruse, Rudolf.
其他作者:
Steinbrecher, Matthias.
面頁冊數:
XIV, 639 p. 324 illus., 42 illus. in color.online resource. :
Contained By:
Springer Nature eBook
標題:
Theory and Algorithms for Application Domains. -
電子資源:
https://doi.org/10.1007/978-3-030-42227-1
ISBN:
9783030422271
Computational Intelligence = A Methodological Introduction /
Kruse, Rudolf.
Computational Intelligence
A Methodological Introduction /[electronic resource] :by Rudolf Kruse, Sanaz Mostaghim, Christian Borgelt, Christian Braune, Matthias Steinbrecher. - 3rd ed. 2022. - XIV, 639 p. 324 illus., 42 illus. in color.online resource. - Texts in Computer Science,1868-095X. - Texts in Computer Science,.
Introduction -- Part I: Neural Networks -- Introduction -- Threshold Logic Units -- General Neural Networks -- Multi-Layer Perceptrons -- Radial Basis Function Networks -- Self-Organizing Maps -- Hopfield Networks -- Recurrent Networks -- Mathematical Remarks for Neural Networks -- Part II: Evolutionary Algorithms -- Introduction to Evolutionary Algorithms -- Elements of Evolutionary Algorithms -- Fundamental Evolutionary Algorithms -- Computational Swarm Intelligence -- Part III: Fuzzy Systems -- Fuzzy Sets and Fuzzy Logic -- The Extension Principle -- Fuzzy Relations -- Similarity Relations -- Fuzzy Control -- Fuzzy Data Analysis -- Part IV: Bayes and Markov Networks -- Introduction to Bayes Networks -- Elements of Probability and Graph Theory -- Decompositions -- Evidence Propagation -- Learning Graphical Models -- Belief Revision -- Decision Graphs.
Computational intelligence comprises concepts, paradigms, algorithms, and implementations of systems that are intended to exhibit intelligent behavior in complex environments. It relies heavily on (at least) nature-inspired methods, which have the advantage that they tolerate incomplete, imprecise and uncertain knowledge and thus also facilitate finding solutions that are approximative, manageable and robust at the same time. Fully updated, this new edition of the authoritative textbook provides a clear and logical introduction to Computational Intelligence, covering the fundamental concepts, algorithms and practical implementations behind efforts to develop systems that exhibit intelligent behavior in complex environments. Rather than aim for completeness, the goal is to give a methodical introduction, supporting fundamental concepts and their implementations with explanation of the theoretical background of proposed problem solutions. Topics and features: Offers new material on deep learning, scalarization, large-scale optimization algorithms, and collective decision-making algorithms Contains numerous classroom-tested examples and definitions Discusses in detail the classical areas of artificial neural networks, fuzzy systems, evolutionary algorithms, and Bayes and Markov networks Reviews the latest developments, including such topics as ant colony optimization and probabilistic graphical models Provides supplementary material, including module descriptions, lecture slides, exercises with solutions, and software tools This seminal textbook is primarily meant as a companion book for lectures on the covered topics in the area of computational intelligence. However, it is also eminently suitable as a guidebook for self-study by students and practitioners from industry and commerce. Dr. Rudolf Kruse is the former leader of the Computational Intelligence Research Group and now Emeritus Professor of the Department of Computer Science at the University of Magdeburg, Germany. Dr. Sanaz Mostaghim is a full Professor of Computer Science and Dr. Christian Braune is a Senior Lecturer at the same institution. Dr. Christian Borgelt is a Professor of Data Science at the Paris Lodron University of Salzburg, Austria. Dr. Matthias Steinbrecher is a Development Architect at SAP SE, Potsdam, Germany.
ISBN: 9783030422271
Standard No.: 10.1007/978-3-030-42227-1doiSubjects--Topical Terms:
1388553
Theory and Algorithms for Application Domains.
LC Class. No.: Q334-342
Dewey Class. No.: 006.3
Computational Intelligence = A Methodological Introduction /
LDR
:04691nam a22004215i 4500
001
1091372
003
DE-He213
005
20220326143728.0
007
cr nn 008mamaa
008
221228s2022 sz | s |||| 0|eng d
020
$a
9783030422271
$9
978-3-030-42227-1
024
7
$a
10.1007/978-3-030-42227-1
$2
doi
035
$a
978-3-030-42227-1
050
4
$a
Q334-342
050
4
$a
TA347.A78
072
7
$a
UYQ
$2
bicssc
072
7
$a
COM004000
$2
bisacsh
072
7
$a
UYQ
$2
thema
082
0 4
$a
006.3
$2
23
100
1
$a
Kruse, Rudolf.
$4
aut
$4
http://id.loc.gov/vocabulary/relators/aut
$3
702278
245
1 0
$a
Computational Intelligence
$h
[electronic resource] :
$b
A Methodological Introduction /
$c
by Rudolf Kruse, Sanaz Mostaghim, Christian Borgelt, Christian Braune, Matthias Steinbrecher.
250
$a
3rd ed. 2022.
264
1
$a
Cham :
$b
Springer International Publishing :
$b
Imprint: Springer,
$c
2022.
300
$a
XIV, 639 p. 324 illus., 42 illus. in color.
$b
online resource.
336
$a
text
$b
txt
$2
rdacontent
337
$a
computer
$b
c
$2
rdamedia
338
$a
online resource
$b
cr
$2
rdacarrier
347
$a
text file
$b
PDF
$2
rda
490
1
$a
Texts in Computer Science,
$x
1868-095X
505
0
$a
Introduction -- Part I: Neural Networks -- Introduction -- Threshold Logic Units -- General Neural Networks -- Multi-Layer Perceptrons -- Radial Basis Function Networks -- Self-Organizing Maps -- Hopfield Networks -- Recurrent Networks -- Mathematical Remarks for Neural Networks -- Part II: Evolutionary Algorithms -- Introduction to Evolutionary Algorithms -- Elements of Evolutionary Algorithms -- Fundamental Evolutionary Algorithms -- Computational Swarm Intelligence -- Part III: Fuzzy Systems -- Fuzzy Sets and Fuzzy Logic -- The Extension Principle -- Fuzzy Relations -- Similarity Relations -- Fuzzy Control -- Fuzzy Data Analysis -- Part IV: Bayes and Markov Networks -- Introduction to Bayes Networks -- Elements of Probability and Graph Theory -- Decompositions -- Evidence Propagation -- Learning Graphical Models -- Belief Revision -- Decision Graphs.
520
$a
Computational intelligence comprises concepts, paradigms, algorithms, and implementations of systems that are intended to exhibit intelligent behavior in complex environments. It relies heavily on (at least) nature-inspired methods, which have the advantage that they tolerate incomplete, imprecise and uncertain knowledge and thus also facilitate finding solutions that are approximative, manageable and robust at the same time. Fully updated, this new edition of the authoritative textbook provides a clear and logical introduction to Computational Intelligence, covering the fundamental concepts, algorithms and practical implementations behind efforts to develop systems that exhibit intelligent behavior in complex environments. Rather than aim for completeness, the goal is to give a methodical introduction, supporting fundamental concepts and their implementations with explanation of the theoretical background of proposed problem solutions. Topics and features: Offers new material on deep learning, scalarization, large-scale optimization algorithms, and collective decision-making algorithms Contains numerous classroom-tested examples and definitions Discusses in detail the classical areas of artificial neural networks, fuzzy systems, evolutionary algorithms, and Bayes and Markov networks Reviews the latest developments, including such topics as ant colony optimization and probabilistic graphical models Provides supplementary material, including module descriptions, lecture slides, exercises with solutions, and software tools This seminal textbook is primarily meant as a companion book for lectures on the covered topics in the area of computational intelligence. However, it is also eminently suitable as a guidebook for self-study by students and practitioners from industry and commerce. Dr. Rudolf Kruse is the former leader of the Computational Intelligence Research Group and now Emeritus Professor of the Department of Computer Science at the University of Magdeburg, Germany. Dr. Sanaz Mostaghim is a full Professor of Computer Science and Dr. Christian Braune is a Senior Lecturer at the same institution. Dr. Christian Borgelt is a Professor of Data Science at the Paris Lodron University of Salzburg, Austria. Dr. Matthias Steinbrecher is a Development Architect at SAP SE, Potsdam, Germany.
650
2 4
$a
Theory and Algorithms for Application Domains.
$3
1388553
650
2 4
$a
Computational Intelligence.
$3
768837
650
2 4
$a
Mathematical and Computational Engineering Applications.
$3
1387767
650
1 4
$a
Artificial Intelligence.
$3
646849
650
0
$a
Computer science.
$3
573171
650
0
$a
Computational intelligence.
$3
568984
650
0
$a
Engineering—Data processing.
$3
1297966
650
0
$a
Engineering mathematics.
$3
562757
650
0
$a
Artificial intelligence.
$3
559380
700
1
$a
Steinbrecher, Matthias.
$e
author.
$4
aut
$4
http://id.loc.gov/vocabulary/relators/aut
$3
1263257
700
1
$a
Braune, Christian.
$e
author.
$4
aut
$4
http://id.loc.gov/vocabulary/relators/aut
$3
1263256
700
1
$a
Borgelt, Christian.
$4
aut
$4
http://id.loc.gov/vocabulary/relators/aut
$3
1071401
700
1
$a
Mostaghim, Sanaz.
$4
aut
$4
http://id.loc.gov/vocabulary/relators/aut
$3
898592
710
2
$a
SpringerLink (Online service)
$3
593884
773
0
$t
Springer Nature eBook
776
0 8
$i
Printed edition:
$z
9783030422264
776
0 8
$i
Printed edition:
$z
9783030422288
776
0 8
$i
Printed edition:
$z
9783030422295
830
0
$a
Texts in Computer Science,
$x
1868-0941
$3
1254292
856
4 0
$u
https://doi.org/10.1007/978-3-030-42227-1
912
$a
ZDB-2-SCS
912
$a
ZDB-2-SXCS
950
$a
Computer Science (SpringerNature-11645)
950
$a
Computer Science (R0) (SpringerNature-43710)
筆 0 讀者評論
多媒體
評論
新增評論
分享你的心得
Export
取書館別
處理中
...
變更密碼[密碼必須為2種組合(英文和數字)及長度為10碼以上]
登入