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

Extensions of dynamic programming fo...

AbouEisha, Hassan.

FindBook

Google Book

Amazon

博客來

Extensions of dynamic programming for combinatorial optimization and data mining

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Extensions of dynamic programming for combinatorial optimization and data mining/ by Hassan AbouEisha ... [et al.].
其他作者:	AbouEisha, Hassan.
出版者:	Cham :Springer International Publishing : : 2019.,
面頁冊數:	xvi, 280 p. :ill. (some col.) , digital ;24 cm.
內容註:	Introduction -- Tools for Study of Pareto Optimal Points -- Some Tools for Decision Tables -- Different Kinds of Decision Trees -- Multi-stage Optimization of Decision Trees with Some Applications -- More Applications of Multi-stage Optimizationof Decision Trees -- Bi-Criteria Optimization Problem for Decision Trees: Cost vs Cost -- Bi-Criteria Optimization Problem for Decision Trees: Cost vs Uncertainty -- Different Kinds of Rules and Systems of Rules.
Contained By:	Springer eBooks
標題:	Dynamic programming. -
電子資源:	http://dx.doi.org/10.1007/978-3-319-91839-6
ISBN:	9783319918396

Extensions of dynamic programming for combinatorial optimization and data mining
Extensions of dynamic programming for combinatorial optimization and data mining[electronic resource] /by Hassan AbouEisha ... [et al.]. - Cham :Springer International Publishing :2019. - xvi, 280 p. :ill. (some col.) , digital ;24 cm. - Intelligent systems reference library,v.1461868-4394 ;. - Intelligent systems reference library ;v.146..

Introduction -- Tools for Study of Pareto Optimal Points -- Some Tools for Decision Tables -- Different Kinds of Decision Trees -- Multi-stage Optimization of Decision Trees with Some Applications -- More Applications of Multi-stage Optimizationof Decision Trees -- Bi-Criteria Optimization Problem for Decision Trees: Cost vs Cost -- Bi-Criteria Optimization Problem for Decision Trees: Cost vs Uncertainty -- Different Kinds of Rules and Systems of Rules.

Dynamic programming is an efficient technique for solving optimization problems. It is based on breaking the initial problem down into simpler ones and solving these sub-problems, beginning with the simplest ones. A conventional dynamic programming algorithm returns an optimal object from a given set of objects. This book develops extensions of dynamic programming, enabling us to (i) describe the set of objects under consideration; (ii) perform a multi-stage optimization of objects relative to different criteria; (iii) count the number of optimal objects; (iv) find the set of Pareto optimal points for bi-criteria optimization problems; and (v) to study relationships between two criteria. It considers various applications, including optimization of decision trees and decision rule systems as algorithms for problem solving, as ways for knowledge representation, and as classifiers; optimization of element partition trees for rectangular meshes, which are used in finite element methods for solving PDEs; and multi-stage optimization for such classic combinatorial optimization problems as matrix chain multiplication, binary search trees, global sequence alignment, and shortest paths. The results presented are useful for researchers in combinatorial optimization, data mining, knowledge discovery, machine learning, and finite element methods, especially those working in rough set theory, test theory, logical analysis of data, and PDE solvers. This book can be used as the basis for graduate courses.

ISBN: 9783319918396

Standard No.: 10.1007/978-3-319-91839-6doiSubjects--Topical Terms:

641303
Dynamic programming.

LC Class. No.: QA402.5

Dewey Class. No.: 519.703

Extensions of dynamic programming for combinatorial optimization and data mining
LDR:03021nmm a2200325 a 4500 001 2176169
003 DE-He213
005 20180522200542.0
006 m d
007 cr nn 008maaau
008 191121s2019 gw s 0 eng d
020 $a 9783319918396 $q (electronic bk.)
020 $a 9783319918389 $q (paper)
024 7 $a 10.1007/978-3-319-91839-6 $2 doi
035 $a 978-3-319-91839-6
040 $a GP $c GP
041 0 $a eng
050 4 $a QA402.5
072 7 $a UYQ $2 bicssc
072 7 $a COM004000 $2 bisacsh
082 0 4 $a 519.703 $2 23
090 $a QA402.5 $b .E96 2019
245 0 0 $a Extensions of dynamic programming for combinatorial optimization and data mining $h [electronic resource] / $c by Hassan AbouEisha ... [et al.].
260 $a Cham : $b Springer International Publishing : $b Imprint: Springer, $c 2019.
300 $a xvi, 280 p. : $b ill. (some col.) , digital ; $c 24 cm.
490 1 $a Intelligent systems reference library, $x 1868-4394 ; $v v.146
505 0 $a Introduction -- Tools for Study of Pareto Optimal Points -- Some Tools for Decision Tables -- Different Kinds of Decision Trees -- Multi-stage Optimization of Decision Trees with Some Applications -- More Applications of Multi-stage Optimizationof Decision Trees -- Bi-Criteria Optimization Problem for Decision Trees: Cost vs Cost -- Bi-Criteria Optimization Problem for Decision Trees: Cost vs Uncertainty -- Different Kinds of Rules and Systems of Rules.
520 $a Dynamic programming is an efficient technique for solving optimization problems. It is based on breaking the initial problem down into simpler ones and solving these sub-problems, beginning with the simplest ones. A conventional dynamic programming algorithm returns an optimal object from a given set of objects. This book develops extensions of dynamic programming, enabling us to (i) describe the set of objects under consideration; (ii) perform a multi-stage optimization of objects relative to different criteria; (iii) count the number of optimal objects; (iv) find the set of Pareto optimal points for bi-criteria optimization problems; and (v) to study relationships between two criteria. It considers various applications, including optimization of decision trees and decision rule systems as algorithms for problem solving, as ways for knowledge representation, and as classifiers; optimization of element partition trees for rectangular meshes, which are used in finite element methods for solving PDEs; and multi-stage optimization for such classic combinatorial optimization problems as matrix chain multiplication, binary search trees, global sequence alignment, and shortest paths. The results presented are useful for researchers in combinatorial optimization, data mining, knowledge discovery, machine learning, and finite element methods, especially those working in rough set theory, test theory, logical analysis of data, and PDE solvers. This book can be used as the basis for graduate courses.
650 0 $a Dynamic programming. $3 641303
650 0 $a Combinatorial optimization. $3 544215
650 0 $a Data mining. $3 562972
650 1 4 $a Engineering. $3 586835
650 2 4 $a Computational Intelligence. $3 1001631
650 2 4 $a Artificial Intelligence (incl. Robotics) $3 890894
700 1 $a AbouEisha, Hassan. $3 3377989
710 2 $a SpringerLink (Online service) $3 836513
773 0 $t Springer eBooks
830 0 $a Intelligent systems reference library ; $v v.146. $3 3377990
856 4 0 $u http://dx.doi.org/10.1007/978-3-319-91839-6
950 $a Engineering (Springer-11647)