Literatura académica sobre el tema "Ordered Weights Average"
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Artículos de revistas sobre el tema "Ordered Weights Average"
Espinoza-Audelo, Luis F., Ernesto León-Castro, Marycruz Olazabal-Lugo, José M. Merigó y Anna M. Gil-Lafuente. "Using Ordered Weighted Average for Weighted Averages Inflation". International Journal of Information Technology & Decision Making 19, n.º 02 (marzo de 2020): 601–28. http://dx.doi.org/10.1142/s0219622020500066.
Texto completoS, Charles y Dr L. Arockiam. "Fuzzy Weighted Ordered Weighted Average-Gaussian Mixture Model for Feature Reduction". INTERNATIONAL JOURNAL OF COMPUTERS & TECHNOLOGY 4, n.º 2 (30 de noviembre de 2005): 694–712. http://dx.doi.org/10.24297/ijct.v4i2c2.4192.
Texto completoRuiz-Morales, Betzabe, Irma Cristina Espitia-Moreno, Victor G. Alfaro-Garcia y Ernesto Leon-Castro. "Sustainable Development Goals Analysis with Ordered Weighted Average Operators". Sustainability 13, n.º 9 (7 de mayo de 2021): 5240. http://dx.doi.org/10.3390/su13095240.
Texto completoYoshida, Yuji. "Ordered Weighted Averages on Intervals and the Sub/Super-Additivity". Journal of Advanced Computational Intelligence and Intelligent Informatics 17, n.º 4 (20 de julio de 2013): 520–25. http://dx.doi.org/10.20965/jaciii.2013.p0520.
Texto completoSavarimuthu, Charles y Arockiam L. "Pairwise Fuzzy Ordered Weighted Average Algorithm-Gaussian Mixture Model for Feature Reduction". INTERNATIONAL JOURNAL OF COMPUTERS & TECHNOLOGY 6, n.º 1 (30 de mayo de 2013): 287–301. http://dx.doi.org/10.24297/ijct.v6i1.4457.
Texto completoOgryczak, Włodzimierz. "Reference Point Method with Importance Weighted Partial Achievements". Journal of Telecommunications and Information Technology, n.º 4 (26 de junio de 2023): 17–25. http://dx.doi.org/10.26636/jtit.2008.4.893.
Texto completoKuz’mina, N. E., S. V. Moiseev, V. I. Krylov, V. A. Yashkir y V. A. Merkulov. "Quantitative determination of the average molecular weights of dextrans by diffusion ordered NMR spectroscopy". Journal of Analytical Chemistry 69, n.º 10 (21 de septiembre de 2014): 953–59. http://dx.doi.org/10.1134/s1061934814100086.
Texto completoWankhade, Sandeep, Manoj Sahni, Cristhian Mellado-Cid y Ernesto Leon-Castro. "Using the Ordered Weighted Average Operator to Gauge Variation in Agriculture Commodities in India". Axioms 12, n.º 10 (18 de octubre de 2023): 985. http://dx.doi.org/10.3390/axioms12100985.
Texto completoFreixas, Josep. "An Aggregation Rule Based on the Binomial Distribution". Mathematics 10, n.º 23 (23 de noviembre de 2022): 4418. http://dx.doi.org/10.3390/math10234418.
Texto completoMajdan, Michał. "On Subjective Trust Management". Journal of Telecommunications and Information Technology, n.º 4 (26 de junio de 2023): 26–31. http://dx.doi.org/10.26636/jtit.2008.4.894.
Texto completoTesis sobre el tema "Ordered Weights Average"
Vo, Thi Quynh Trang. "Algorithms and Machine Learning for fair and classical combinatorial optimization". Electronic Thesis or Diss., Université Clermont Auvergne (2021-...), 2024. http://www.theses.fr/2024UCFA0035.
Texto completoCombinatorial optimization is a field of mathematics that searches for an optimal solution in a finite set of objects. It has crucial applications in many fields, including applied mathematics, software engineering, theoretical computer science, and machine learning. extit{Branch-and-cut} is one of the most widely-used algorithms for solving combinatorial optimization problems exactly. In this thesis, we focus on the computational aspects of branch-and-cut when studying two critical dimensions of combinatorial optimization: extit{the fairness of solutions} and extit{the integration of machine learning}.In Partef{part:1} (Chaptersef{chap:bnc-btsp} andef{chap:owa}), we study two common approaches to deal with the issue of fairness in combinatorial optimization, which has gained significant attention in the past decades. The first approach is extit{balanced combinatorial optimization}, which finds a fair solution by minimizing the difference between the largest and smallest components used. Due to the difficulties in bounding these components, to the best of our knowledge, no general exact framework based on mixed-integer linear programming (MILP) has been proposed for balanced combinatorial optimization. To address this gap, in Chapteref{chap:bnc-btsp}, we present a branch-and-cut algorithm and a novel class of local cutting planes tailored for balanced combinatorial optimization problems. We demonstrate the effectiveness of the proposed framework in the Balanced Traveling Salesman Problem. Additionally, we introduce bounding algorithms and mechanisms to fix variables to accelerate performance further.The second approach to handling the issue of fairness is extit{Ordered Weighted Average (OWA) combinatorial optimization}, which integrates the OWA operator into the objective function. Due to the ordering operator, OWA combinatorial optimization is nonlinear, even if its original constraints are linear. Two MILP formulations of different sizes have been introduced in the literature to linearize the OWA operator. However, which formulation performs best for OWA combinatorial optimization remains uncertain, as integrating the linearization methods may introduce additional difficulties. In Chapteref{chap:owa}, we provide theoretical and empirical comparisons of the two MILP formulations for OWA combinatorial optimization. In particular, we prove that the formulations are equivalent in terms of the linear programming relaxation. We empirically show that for OWA combinatorial optimization problems, the formulation with more variables can be solved faster with branch-and-cut.In Partef{part:2} (Chapteref{chap:mlbnc}), we develop methods for applying machine learning to enhance fundamental decision problems in branch-and-cut, with a focus on cut generation. Cut generation refers to the decision of whether to generate cuts or to branch at each node of the search tree. We empirically demonstrate that this decision significantly impacts branch-and-cut performance, especially for combinatorial cuts that exploit the facial structure of the convex hull of feasible solutions. We then propose a general framework combining supervised and reinforcement learning to learn effective strategies for generating combinatorial cuts in branch-and-cut. Our framework has two components: a cut detector to predict cut existence and a cut evaluator to choose between generating cuts and branching. Finally, we provide experimental results showing that the proposed method outperforms commonly used strategies for cut generation, even on instances larger than those used for training
Colliri, Tiago Santos. "Avaliação de preços de ações: proposta de um índice baseado nos preços históricos ponderados pelo volume, por meio do uso de modelagem computacional". Universidade de São Paulo, 2013. http://www.teses.usp.br/teses/disponiveis/100/100132/tde-07072013-015903/.
Texto completoThe importance of considering the volumes to analyze stock prices movements can be considered as a well-accepted practice in the financial area. However, when we look at the scientific production in this field, we still cannot find a unified model that includes volume and price variations for stock prices assessment purposes. In this paper we present a computer model that could fulfill this gap, proposing a new index to evaluate stock prices based on their historical prices and volumes traded. The aim of the model is to estimate the current proportions of the total volume of shares available in the market from a stock distributed according with their respective prices traded in the past. In order to do so, we made use of dynamic financial modeling and applied it to real financial data from the Sao Paulo Stock Exchange (Bovespa) and also to simulated data which was generated trough an order book model. The value of our index varies based on the difference between the current proportion of shares traded in the past for a price above the current price of the stock and its respective counterpart, which would be the proportion of shares traded in the past for a price below the current price of the stock. Besides the model can be considered mathematically very simple, it was able to improve significantly the financial performance of agents operating with real market data and with simulated data, which contributes to demonstrate its rationale and its applicability. Based on the results obtained, and also on the very intuitive logic of our model, we believe that the index proposed here can be very useful to help investors on the activity of determining ideal price ranges for buying and selling stocks in the financial market.
Libros sobre el tema "Ordered Weights Average"
Horing, Norman J. Morgenstern. Thermodynamic Green’s Functions and Spectral Structure. Oxford University Press, 2018. http://dx.doi.org/10.1093/oso/9780198791942.003.0007.
Texto completoBack, Kerry E. Alternative Preferences. Oxford University Press, 2017. http://dx.doi.org/10.1093/acprof:oso/9780190241148.003.0025.
Texto completoSkiba, Grzegorz. Fizjologiczne, żywieniowe i genetyczne uwarunkowania właściwości kości rosnących świń. The Kielanowski Institute of Animal Physiology and Nutrition, Polish Academy of Sciences, 2020. http://dx.doi.org/10.22358/mono_gs_2020.
Texto completoCapítulos de libros sobre el tema "Ordered Weights Average"
León-Castro, Ernesto, Fabio Blanco-Mesa y José M. Merigó. "Weighted Averages in the Ordered Weighted Average Inflation". En Advances in Intelligent Systems and Computing, 87–95. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-21920-8_8.
Texto completoWu, Dongrui y Jian Huang. "Ordered Novel Weighted Averages". En Type-2 Fuzzy Logic and Systems, 25–47. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-72892-6_2.
Texto completoYoshida, Yuji. "An Ordered Weighted Average with a Truncation Weight on Intervals". En Modeling Decisions for Artificial Intelligence, 45–55. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-34620-0_6.
Texto completoCornelis, Chris, Nele Verbiest y Richard Jensen. "Ordered Weighted Average Based Fuzzy Rough Sets". En Lecture Notes in Computer Science, 78–85. Berlin, Heidelberg: Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-642-16248-0_16.
Texto completoKlimo, Martin, Ondrej Škvarek, Juraj Smieško, Stanislav Foltán y Ondrej Šuch. "Vowel Recognition Supported by Ordered Weighted Average". En Advances in Intelligent Systems and Computing, 247–53. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-10783-7_27.
Texto completoMitchell, H. B. "Data Mining Using a Probabilistic Weighted Ordered Weighted Average (PWOWA) Operator". En Information Fusion in Data Mining, 41–57. Berlin, Heidelberg: Springer Berlin Heidelberg, 2003. http://dx.doi.org/10.1007/978-3-540-36519-8_4.
Texto completoShams, Parham, Aurélie Beynier, Sylvain Bouveret y Nicolas Maudet. "Minimizing and Balancing Envy Among Agents Using Ordered Weighted Average". En Algorithmic Decision Theory, 289–303. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-87756-9_19.
Texto completoKacprzyk, Janusz y Mario Fedrizzi. "Consensus Degrees Under Fuzziness via Ordered Weighted Average (OWA) Operators". En Fuzzy Logic and its Applications to Engineering, Information Sciences, and Intelligent Systems, 447–53. Dordrecht: Springer Netherlands, 1995. http://dx.doi.org/10.1007/978-94-009-0125-4_44.
Texto completoMerigó, José M., Montserrat Guillén y José M. Sarabia. "A Generalization of the Variance by Using the Ordered Weighted Average". En Modeling and Simulation in Engineering, Economics, and Management, 222–31. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-38279-6_24.
Texto completoVerbiest, Nele, Chris Cornelis y Francisco Herrera. "OWA-FRPS: A Prototype Selection Method Based on Ordered Weighted Average Fuzzy Rough Set Theory". En Lecture Notes in Computer Science, 180–90. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-41218-9_19.
Texto completoActas de conferencias sobre el tema "Ordered Weights Average"
Darmstadt, Patrick y Mark Robuck. "Composites for Advanced Drive Systems, a Systems Analysis Revolutionary Vertical Lift Technology (RVLT)". En Vertical Flight Society 74th Annual Forum & Technology Display, 1–16. The Vertical Flight Society, 2018. http://dx.doi.org/10.4050/f-0074-2018-12862.
Texto completoWhalen, Thomas. "Additive Weighted Ordered Weighted Average". En NAFIPS 2007 - 2007 Annual Meeting of the North American Fuzzy Information Processing Society. IEEE, 2007. http://dx.doi.org/10.1109/nafips.2007.383871.
Texto completoWu, Dongrui y Jerry M. Mendel. "Ordered fuzzy weighted averages and ordered linguistic weighted averages". En 2010 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE). IEEE, 2010. http://dx.doi.org/10.1109/fuzzy.2010.5584479.
Texto completoMarin, Lucas, Jose M. Merigo, Aida Valls, Antonio Moreno y David Isern. "Induced Unbalanced Linguistic Ordered Weighted Average". En 7th conference of the European Society for Fuzzy Logic and Technology. Paris, France: Atlantis Press, 2011. http://dx.doi.org/10.2991/eusflat.2011.88.
Texto completoMerigo, Jose M., Nabil Alrajeh y Marta Peris-Ortiz. "Induced aggregation operators in the ordered weighted average sum". En 2016 IEEE Symposium Series on Computational Intelligence (SSCI). IEEE, 2016. http://dx.doi.org/10.1109/ssci.2016.7850148.
Texto completoVo, Thi Quynh Trang, Mourad Baiou, Viet Hung Nguyen y Paul Weng. "A comparative study of linearization methods for Ordered Weighted Average". En 2022 12th International Workshop on Resilient Networks Design and Modeling (RNDM). IEEE, 2022. http://dx.doi.org/10.1109/rndm55901.2022.9927720.
Texto completoMeenachi, L., J. Jayanth Raghul, C. Mohan Raj y B. Kathiravan. "Diagnosis of medical dataset using fuzzy-rough ordered weighted average classification". En 2017 4th International Conference on Innovations in Information, Embedded and Communication Systems (ICIIECS). IEEE, 2017. http://dx.doi.org/10.1109/iciiecs.2017.8275922.
Texto completoHai Wang, Yan Zhang y Gang Qian. "Multiple binary classifiers fusion using induced intuitionistic fuzzy ordered weighted average operator". En 2011 International Conference on Information and Automation (ICIA). IEEE, 2011. http://dx.doi.org/10.1109/icinfa.2011.5948993.
Texto completoDinh, My H., James Kotary y Ferdinando Fioretto. "Learning Fair Ranking Policies via Differentiable Optimization of Ordered Weighted Averages". En FAccT '24: The 2024 ACM Conference on Fairness, Accountability, and Transparency. New York, NY, USA: ACM, 2024. http://dx.doi.org/10.1145/3630106.3661932.
Texto completoZhao, Xinye, Xiaowei Zhang, Wenming Zhang y Fan Yang. "Some induced generalized ordered weighted power average operators within intuitionistic trapezoidal fuzzy setting". En 2016 12th International Conference on Natural Computation and 13th Fuzzy Systems and Knowledge Discovery (ICNC-FSKD). IEEE, 2016. http://dx.doi.org/10.1109/fskd.2016.7603301.
Texto completoInformes sobre el tema "Ordered Weights Average"
Zhang, Hongbin B., David J. Bonfil y Shahal Abbo. Genomics Tools for Legume Agronomic Gene Mapping and Cloning, and Genome Analysis: Chickpea as a Model. United States Department of Agriculture, marzo de 2003. http://dx.doi.org/10.32747/2003.7586464.bard.
Texto completoMizrach, Amos, Sydney L. Spahr, Ephraim Maltz, Michael R. Murphy, Zeev Schmilovitch, Jan E. Novakofski, Uri M. Peiper et al. Ultrasonic Body Condition Measurements for Computerized Dairy Management Systems. United States Department of Agriculture, 1993. http://dx.doi.org/10.32747/1993.7568109.bard.
Texto completoJob, Jacob. Mesa Verde National Park: Acoustic monitoring report. National Park Service, julio de 2021. http://dx.doi.org/10.36967/nrr-2286703.
Texto completoMaycock, Barry, Cath Mulholland, Emma French y Joseph Shavila. Rapid Risk Assessment: What is the risk from microcystins in the edible flesh of fish caught from Lough Neagh? Food Standards Agency, marzo de 2024. http://dx.doi.org/10.46756/sci.fsa.slz868.
Texto completoIntroduction Success of Less Common Species from the Genus Berberis L. Ukrainian Journal of Ecology, 2019. http://dx.doi.org/10.31812/123456789/3641.
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