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010   $a9783031970702
024 7 $a10.1007/978-3-031-97070-2
035   $a(CKB)43368478900041
035   $a(MiAaPQ)EBC32419906
035   $a(Au-PeEL)EBL32419906
035   $a(DE-He213)978-3-031-97070-2
035   $a(EXLCZ)9943368478900041
100   $a20251117d2026      u|         0
101 0 $aeng
135   $aur|||||||||||
181   $ctxt$2rdacontent
182   $cc$2rdamedia
183   $acr$2rdacarrier
200 10$aChoquet Capacities and Fuzzy Integrals /$fby Gleb Beliakov, Simon James, Jian-Zhang Wu
205   $a1st ed. 2026.
210  1$aCham :$cSpringer Nature Switzerland :$cImprint: Springer,$d2026.
215   $a1 online resource (555 pages)
225 1 $aTheory and Applications of Computability, In cooperation with the Association Computability in Europe,$x2190-6203
311 08$a9783031970696 
327   $aIntroduction -- Types of Capacities -- Value and Interaction Indices -- Representations -- Fuzzy Intergrals -- Sparse Capacities -- Symmetric Fuzzy Measures: OWA -- Learning Capacities -- Optimisation Models Based on Fuzzy Integrals -- Random Sampling of the Capacities.
330   $aChoquet capacities, which provide the weighting mechanism for the Choquet and other fuzzy integrals, model synergistic and antagonistic interactions between variables by assigning value to all subsets rather than individual inputs. This book provides a detailed overview of the background concepts relating to capacities and their role in fuzzy integration and aggregation, then presents specialised chapters on most recent results in learning, random sampling and optimization that involve Choquet capacities. Topics and features: · Fundamentals of Choquet capacities (fuzzy measures) and their use in modeling importance and interaction between variables · Definitions, properties and mappings between alternative representations that allow capacities and fuzzy integrals to be interpreted and applied in different settings · Capacity learning formulations that allow the diverse types to be elicited from datasets or according to user-specified requirements · Recent findings related to random sampling and optimisation with Choquet integral objectives This book includes illustrative examples and guidance for implementation, including an appendix detailing functions found in the pyfmtools software library. It aims to be useful for practitioners and researchers in decision and data-driven fields, or those who wish to apply these emerging tools to new problems. The authors are all affiliated with the School of Information Technology at Deakin University, Australia. Gleb Beliakov is a professor, Simon James< is an Associate Professor, and Jian-Zhang Wu is a Research Fellow. .
410  0$aTheory and Applications of Computability, In cooperation with the Association Computability in Europe,$x2190-6203
606   $aComputer science
606   $aIntegral equations
606   $aFunctions, Special
606   $aTheory of Computation
606   $aIntegral Equations
606   $aComputer Science Logic and Foundations of Programming
606   $aSpecial Functions
615  0$aComputer science.
615  0$aIntegral equations.
615  0$aFunctions, Special.
615 14$aTheory of Computation.
615 24$aIntegral Equations.
615 24$aComputer Science Logic and Foundations of Programming.
615 24$aSpecial Functions.
676   $a004.0151
700   $aBeliakov$b Gleb$0760797
801  0$bMiAaPQ
801  1$bMiAaPQ
801  2$bMiAaPQ
906   $aBOOK
912   $a9911047803803321
996   $aChoquet Capacities and Fuzzy Integrals$94471230
997   $aUNINA