東華大學圖書館 |

Language: English

Help

回圖書館首頁

手機版館藏查詢

Back

Switch To: Labeled | MARC Mode | ISBD

Evolutionary learning = advances in ...

Zhou, Zhi-Hua.

Linked to FindBook

Google Book

Amazon

博客來

Evolutionary learning = advances in theories and algorithms /

Record Type:	Electronic resources : Monograph/item
Title/Author:	Evolutionary learning/ by Zhi-Hua Zhou, Yang Yu, Chao Qian.
Reminder of title:	advances in theories and algorithms /
Author:	Zhou, Zhi-Hua.
other author:	Yu, Yang.
Published:	Singapore :Springer Singapore : : 2019.,
Description:	xii, 361 p. :ill., digital ;24 cm.
[NT 15003449]:	1.Introduction -- 2. Preliminaries -- 3. Running Time Analysis: Convergence-based Analysis -- 4. Running Time Analysis: Switch Analysis -- 5. Running Time Analysis: Comparison and Unification -- 6. Approximation Analysis: SEIP -- 7. Boundary Problems of EAs -- 8. Recombination -- 9. Representation -- 10. Inaccurate Fitness Evaluation -- 11. Population -- 12. Constrained Optimization -- 13. Selective Ensemble -- 14. Subset Selection -- 15. Subset Selection: k-Submodular Maximization -- 16. Subset Selection: Ratio Minimization -- 17. Subset Selection: Noise -- 18. Subset Selection: Acceleration.
Contained By:	Springer eBooks
Subject:	Machine learning. -
Online resource:	https://doi.org/10.1007/978-981-13-5956-9
ISBN:	9789811359569

Evolutionary learning = advances in theories and algorithms /
Zhou, Zhi-Hua.

Evolutionary learningadvances in theories and algorithms /[electronic resource] :by Zhi-Hua Zhou, Yang Yu, Chao Qian. - Singapore :Springer Singapore :2019. - xii, 361 p. :ill., digital ;24 cm.

1.Introduction -- 2. Preliminaries -- 3. Running Time Analysis: Convergence-based Analysis -- 4. Running Time Analysis: Switch Analysis -- 5. Running Time Analysis: Comparison and Unification -- 6. Approximation Analysis: SEIP -- 7. Boundary Problems of EAs -- 8. Recombination -- 9. Representation -- 10. Inaccurate Fitness Evaluation -- 11. Population -- 12. Constrained Optimization -- 13. Selective Ensemble -- 14. Subset Selection -- 15. Subset Selection: k-Submodular Maximization -- 16. Subset Selection: Ratio Minimization -- 17. Subset Selection: Noise -- 18. Subset Selection: Acceleration.

Many machine learning tasks involve solving complex optimization problems, such as working on non-differentiable, non-continuous, and non-unique objective functions; in some cases it can prove difficult to even define an explicit objective function. Evolutionary learning applies evolutionary algorithms to address optimization problems in machine learning, and has yielded encouraging outcomes in many applications. However, due to the heuristic nature of evolutionary optimization, most outcomes to date have been empirical and lack theoretical support. This shortcoming has kept evolutionary learning from being well received in the machine learning community, which favors solid theoretical approaches. Recently there have been considerable efforts to address this issue. This book presents a range of those efforts, divided into four parts. Part I briefly introduces readers to evolutionary learning and provides some preliminaries, while Part II presents general theoretical tools for the analysis of running time and approximation performance in evolutionary algorithms. Based on these general tools, Part III presents a number of theoretical findings on major factors in evolutionary optimization, such as recombination, representation, inaccurate fitness evaluation, and population. In closing, Part IV addresses the development of evolutionary learning algorithms with provable theoretical guarantees for several representative tasks, in which evolutionary learning offers excellent performance.

ISBN: 9789811359569

Standard No.: 10.1007/978-981-13-5956-9doiSubjects--Topical Terms:

533906
Machine learning.

LC Class. No.: Q325.5 / .Z46 2019

Dewey Class. No.: 006.31

Evolutionary learning = advances in theories and algorithms /
LDR:03092nmm a2200325 a 4500 001 2191191
003 DE-He213
005 20190523151322.0
006 m d
007 cr nn 008maaau
008 200504s2019 si s 0 eng d
020 $a 9789811359569 $q (electronic bk.)
020 $a 9789811359552 $q (paper)
024 7 $a 10.1007/978-981-13-5956-9 $2 doi
035 $a 978-981-13-5956-9
040 $a GP $c GP
041 0 $a eng
050 4 $a Q325.5 $b .Z46 2019
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 .Z63 2019
100 1 $a Zhou, Zhi-Hua. $3 927817
245 1 0 $a Evolutionary learning $h [electronic resource] : $b advances in theories and algorithms / $c by Zhi-Hua Zhou, Yang Yu, Chao Qian.
260 $a Singapore : $b Springer Singapore : $b Imprint: Springer, $c 2019.
300 $a xii, 361 p. : $b ill., digital ; $c 24 cm.
505 0 $a 1.Introduction -- 2. Preliminaries -- 3. Running Time Analysis: Convergence-based Analysis -- 4. Running Time Analysis: Switch Analysis -- 5. Running Time Analysis: Comparison and Unification -- 6. Approximation Analysis: SEIP -- 7. Boundary Problems of EAs -- 8. Recombination -- 9. Representation -- 10. Inaccurate Fitness Evaluation -- 11. Population -- 12. Constrained Optimization -- 13. Selective Ensemble -- 14. Subset Selection -- 15. Subset Selection: k-Submodular Maximization -- 16. Subset Selection: Ratio Minimization -- 17. Subset Selection: Noise -- 18. Subset Selection: Acceleration.
520 $a Many machine learning tasks involve solving complex optimization problems, such as working on non-differentiable, non-continuous, and non-unique objective functions; in some cases it can prove difficult to even define an explicit objective function. Evolutionary learning applies evolutionary algorithms to address optimization problems in machine learning, and has yielded encouraging outcomes in many applications. However, due to the heuristic nature of evolutionary optimization, most outcomes to date have been empirical and lack theoretical support. This shortcoming has kept evolutionary learning from being well received in the machine learning community, which favors solid theoretical approaches. Recently there have been considerable efforts to address this issue. This book presents a range of those efforts, divided into four parts. Part I briefly introduces readers to evolutionary learning and provides some preliminaries, while Part II presents general theoretical tools for the analysis of running time and approximation performance in evolutionary algorithms. Based on these general tools, Part III presents a number of theoretical findings on major factors in evolutionary optimization, such as recombination, representation, inaccurate fitness evaluation, and population. In closing, Part IV addresses the development of evolutionary learning algorithms with provable theoretical guarantees for several representative tasks, in which evolutionary learning offers excellent performance.
650 0 $a Machine learning. $3 533906
650 1 4 $a Artificial Intelligence. $3 769149
650 2 4 $a Algorithm Analysis and Problem Complexity. $3 891007
650 2 4 $a Math Applications in Computer Science. $3 891004
700 1 $a Yu, Yang. $3 1672361
700 1 $a Qian, Chao. $3 3410317
710 2 $a SpringerLink (Online service) $3 836513
773 0 $t Springer eBooks
856 4 0 $u https://doi.org/10.1007/978-981-13-5956-9
950 $a Computer Science (Springer-11645)