標(biāo)題: Titlebook: Algorithmic Decision Making with Python Resources; From Multicriteria P Raymond Bisdorff Textbook 2022 The Editor(s) (if applicable) and Th [打印本頁(yè)] 作者: CANTO 時(shí)間: 2025-3-21 16:55
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書目名稱Algorithmic Decision Making with Python Resources網(wǎng)絡(luò)公開度學(xué)科排名
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書目名稱Algorithmic Decision Making with Python Resources讀者反饋
書目名稱Algorithmic Decision Making with Python Resources讀者反饋學(xué)科排名
作者: 商業(yè)上 時(shí)間: 2025-3-21 22:00
Working with Outranking Digraphsiteria performance tableau, we construct the corresponding bipolar-valued outranking relation from pairwise comparisons. The resulting bipolar-valued outranking characteristics may be recoded. Finally, the codual outranking digraph gives us the associated strict outranking relation.作者: CLEFT 時(shí)間: 2025-3-22 01:17 作者: 河流 時(shí)間: 2025-3-22 08:14
How to Create a New Multiple-Criteria Performance Tableau six performance criteria. We discuss in detail editing the decision alternatives, the decision objectives, the family of performance criteria, and finally, the evaluations of the decision alternatives on the performance criteria.作者: 不滿分子 時(shí)間: 2025-3-22 11:04 作者: 相同 時(shí)間: 2025-3-22 14:07 作者: jagged 時(shí)間: 2025-3-22 20:45
HPC Ranking of Big Performance Tableauxs or millions of records. To effectively compute rankings from performance tableaux of these sizes, we propose in this chapter a collection of C-compiled and optimised . modules that may be run on HPC equipment as available, for instance, at the University of Luxembourg.作者: 偽證 時(shí)間: 2025-3-23 00:40 作者: 問(wèn)到了燒瓶 時(shí)間: 2025-3-23 05:23 作者: cavity 時(shí)間: 2025-3-23 06:30 作者: Foregery 時(shí)間: 2025-3-23 12:35 作者: Gourmet 時(shí)間: 2025-3-23 16:09 作者: CRUMB 時(shí)間: 2025-3-23 21:01 作者: 大火 時(shí)間: 2025-3-24 01:53
Ann T. Tai,Algirdas Avi?ienis,John F. Meyere, we consider pairwise comparisons of election candidates and balance the number of times the first beats the second against the number of times the second beats the first. Thus we obtain the majority margins digraph, in fact a bipolar-valued digraph. When the voters express contradictory linear vo作者: FLAX 時(shí)間: 2025-3-24 04:53
https://doi.org/10.1007/978-3-7091-4009-3ia, into . quantile equivalence classes. The sorting algorithm is based on pairwise outranking characteristics involving the quantile class limits observed on each criterion. Thus we may implement a weak ordering algorithm of complexity .(.).作者: 帳單 時(shí)間: 2025-3-24 07:55
Denial of Service: A Perspectivee quantiles learned from historical performance data gathered from similar decision alternatives observed in the past. We show how to learn performance quantiles from such historical performance tableaux. New performance records may now be rated with respect to these quantile norms.作者: AMITY 時(shí)間: 2025-3-24 11:21
Rajashekar Kailar,Virgil D. Gligor,Li Gongs or millions of records. To effectively compute rankings from performance tableaux of these sizes, we propose in this chapter a collection of C-compiled and optimised . modules that may be run on HPC equipment as available, for instance, at the University of Luxembourg.作者: 必死 時(shí)間: 2025-3-24 15:01 作者: 嬰兒 時(shí)間: 2025-3-24 21:31
Industrial Use of Formal MethodsSeveral hundred academic CS Departments, from all over the world, were ranked that year following an overall numerical score based on the weighted average of five performance criteria: . (the learning environment, 30%), . (volume, income and reputation, 30%), . (research influence, 27.5%), . (staff,作者: 貿(mào)易 時(shí)間: 2025-3-25 01:41 作者: 杠桿 時(shí)間: 2025-3-25 05:50
Raymond BisdorffProvides first hand step-by-step tutorials for solving didactical and practical decision problems with multiple criteria.Introduces main objects available in the Digraph3 collection of Python3 modules作者: 舊石器 時(shí)間: 2025-3-25 10:08 作者: 易于 時(shí)間: 2025-3-25 12:00 作者: NOCT 時(shí)間: 2025-3-25 16:13
Compiler Correctness and Input/OutputThe chapter describes the . resources for generating random multiple-criteria performance tableaux like a . tableau, a three Objectives—., ., and .—tableau, and an . performance tableau.作者: 弄皺 時(shí)間: 2025-3-25 22:43 作者: evasive 時(shí)間: 2025-3-26 01:36 作者: capsule 時(shí)間: 2025-3-26 06:41
Working with the , Python ResourcesThe chapter is devoted to a first contact with the . Python resources. Following the installation instructions, we list the main Python modules with their purpose and eventually illustrate in a first Python terminal session how to generate, save, and inspect a random crisp digraph.作者: 社團(tuán) 時(shí)間: 2025-3-26 09:36
Generating Random Performance TableauxThe chapter describes the . resources for generating random multiple-criteria performance tableaux like a . tableau, a three Objectives—., ., and .—tableau, and an . performance tableau.作者: 肥料 時(shí)間: 2025-3-26 15:05 作者: HEPA-filter 時(shí)間: 2025-3-26 19:40
ExercisesIn this chapter, we propose a series of decision problems of various difficulties which may serve as exercises and exam questions for an Algorithmic Decision Theory or Multiple-Criteria Decision Analysis course. They cover ., ., and . decision problems.作者: 動(dòng)物 時(shí)間: 2025-3-26 22:28 作者: 一回合 時(shí)間: 2025-3-27 03:55 作者: browbeat 時(shí)間: 2025-3-27 08:31
Algorithmic Decision Making with Python Resources978-3-030-90928-4Series ISSN 0884-8289 Series E-ISSN 2214-7934 作者: 波動(dòng) 時(shí)間: 2025-3-27 11:19 作者: tenuous 時(shí)間: 2025-3-27 15:20
https://doi.org/10.1007/978-3-7091-4009-3iteria performance tableau, we construct the corresponding bipolar-valued outranking relation from pairwise comparisons. The resulting bipolar-valued outranking characteristics may be recoded. Finally, the codual outranking digraph gives us the associated strict outranking relation.作者: Entirety 時(shí)間: 2025-3-27 19:37 作者: narcotic 時(shí)間: 2025-3-27 22:43 作者: Horizon 時(shí)間: 2025-3-28 04:38
https://doi.org/10.1007/978-3-7091-4009-3ia, into . quantile equivalence classes. The sorting algorithm is based on pairwise outranking characteristics involving the quantile class limits observed on each criterion. Thus we may implement a weak ordering algorithm of complexity .(.).作者: 歹徒 時(shí)間: 2025-3-28 09:28
Denial of Service: A Perspectivee quantiles learned from historical performance data gathered from similar decision alternatives observed in the past. We show how to learn performance quantiles from such historical performance tableaux. New performance records may now be rated with respect to these quantile norms.作者: 雪上輕舟飛過(guò) 時(shí)間: 2025-3-28 13:22 作者: 小畫像 時(shí)間: 2025-3-28 15:50
https://doi.org/10.1007/978-3-7091-9396-9 the choice of her future University studies. We present Alice’s performance tableau—potential foreign language study programs, her decision objectives, performance criteria and performance evaluations—and build a best choice recommendation for her. A thorough robustness analysis confirms a very best choice.作者: Microaneurysm 時(shí)間: 2025-3-28 20:53 作者: Thyroid-Gland 時(shí)間: 2025-3-29 01:51
Working with Bipolar-Valued Digraphsof a randomly valued digraph, we illustrate some basic digraph manipulation methods, like drawing the digraph, dividing the digraph into its asymmetric and symmetric parts, separating the border from the inner part, computing associated dual, converse and codual digraphs, and operating symmetric and作者: Connotation 時(shí)間: 2025-3-29 03:58
Working with Outranking Digraphsiteria performance tableau, we construct the corresponding bipolar-valued outranking relation from pairwise comparisons. The resulting bipolar-valued outranking characteristics may be recoded. Finally, the codual outranking digraph gives us the associated strict outranking relation.作者: Camouflage 時(shí)間: 2025-3-29 09:03
Building a Best Choice Recommendationexplore the given performance tableau and compute the corresponding outranking digraph. After presenting the pragmatic principles that govern our best choice recommendation algorithm we solve the best office location choice problem.作者: Fluctuate 時(shí)間: 2025-3-29 14:42 作者: Creatinine-Test 時(shí)間: 2025-3-29 19:23
Who Wins the Election?e, we consider pairwise comparisons of election candidates and balance the number of times the first beats the second against the number of times the second beats the first. Thus we obtain the majority margins digraph, in fact a bipolar-valued digraph. When the voters express contradictory linear vo作者: triptans 時(shí)間: 2025-3-29 19:50 作者: 袖章 時(shí)間: 2025-3-30 01:47
Rating-by-Ranking with Learned Performance Quantile Normse quantiles learned from historical performance data gathered from similar decision alternatives observed in the past. We show how to learn performance quantiles from such historical performance tableaux. New performance records may now be rated with respect to these quantile norms.作者: 南極 時(shí)間: 2025-3-30 07:08 作者: Isthmus 時(shí)間: 2025-3-30 10:03 作者: bleach 時(shí)間: 2025-3-30 14:43
The Best Academic Computer Science Depts: A Ranking Case StudySeveral hundred academic CS Departments, from all over the world, were ranked that year following an overall numerical score based on the weighted average of five performance criteria: . (the learning environment, 30%), . (volume, income and reputation, 30%), . (research influence, 27.5%), . (staff,作者: 使迷醉 時(shí)間: 2025-3-30 17:53 作者: 逢迎春日 時(shí)間: 2025-3-30 23:50 作者: 怎樣才咆哮 時(shí)間: 2025-3-31 03:33
Textbook 2022atings using incommensurable criteria. ..The book’s third part presents three real-world decision case studies, while the fourth part addresses more advanced topics, such as computing ordinal correlations between bipolar-valued outranking digraphs, computing kernels in bipolar-valued digraphs, testi
