Articles with public access mandates - Vladik KreinovichLearn more
Not available anywhere: 3
Department of Computer Science, University of Texas at El Paso, El Paso, TX, USA
V Kreinovich
Mandates: US National Science Foundation
Fuzzy Approach to Optimal Placement of Health Centers
JC Figueroa-García, C Franco, V Kreinovich
Fuzzy Techniques: Theory and Applications: Proceedings of the 2019 Joint …, 2019
Mandates: US National Science Foundation
Почему задача поиска локального оптимума иногда сложнее, чем задача поиска глобального оптимума
O Kosheleva, V Kreinovich
Математические структуры и моделирование, 39-43, 2016
Mandates: US National Science Foundation
Available somewhere: 1,088
Imprecise probabilities in engineering analyses
M Beer, S Ferson, V Kreinovich
Mechanical systems and signal processing 37 (1-2), 4-29, 2013
Mandates: US National Institutes of Health
Why 70/30 or 80/20 relation between training and testing sets: A pedagogical explanation
A Gholamy, V Kreinovich, O Kosheleva
Int. J. Intell. Technol. Appl. Stat 11 (2), 105-111, 2018
Mandates: US National Science Foundation
A simple probabilistic explanation of term frequency-inverse document frequency (tf-idf) heuristic (and variations motivated by this explanation)
L Havrlant, V Kreinovich
International Journal of General Systems 46 (1), 27-36, 2017
Mandates: US National Science Foundation
Decision making beyond arrow's “impossibility theorem,” with the analysis of effects of collusion and mutual attraction
HT Nguyen, O Kosheleva, V Kreinovich
International Journal of Intelligent Systems 24 (1), 27-47, 2009
Mandates: US National Institutes of Health
Decision making under interval uncertainty (and beyond)
V Kreinovich
Human-centric decision-making models for social sciences, 163-193, 2014
Mandates: US National Institutes of Health
Why ℓ1 is a good approximation to ℓ0: A geometric explanation
C Ramirez, V Kreinovich, M Argaez
Mandates: US National Institutes of Health
Security games with interval uncertainty
C Kiekintveld, T Islam, V Kreinovich
Mandates: US National Institutes of Health
Why triangular and trapezoid membership functions: A simple explanation
V Kreinovich, O Kosheleva, SN Shahbazova
Recent developments in fuzzy logic and fuzzy sets: dedicated to Lotfi A …, 2020
Mandates: US National Science Foundation
Bayesian approach for inconsistent information
M Stein, M Beer, V Kreinovich
Information sciences 245, 96-111, 2013
Mandates: US National Institutes of Health
Solving equations (and systems of equations) under uncertainty: how different practical problems lead to different mathematical and computational formulations
V Kreinovich
Granular Computing 1, 171-179, 2016
Mandates: US National Science Foundation
Fuzzy transforms of higher order approximate derivatives: a theorem
I Perfilieva, V Kreinovich
Fuzzy Sets and Systems 180 (1), 55-68, 2011
Mandates: US National Institutes of Health
Towards decision making under interval, set-valued, fuzzy, and Z-number uncertainty: a fair price approach
J Lorkowski, V Kreinovich, R Aliev
2014 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE), 2244-2253, 2014
Mandates: US National Institutes of Health
Why quantum (wave probability) models are a good description of many non-quantum complex systems, and how to go beyond quantum models
M Svítek, O Kosheleva, V Kreinovich, TN Nguyen
Beyond Traditional Probabilistic Methods in Economics 2, 168-175, 2019
Mandates: US National Science Foundation
Quantum approach explains the need for expert knowledge: on the example of econometrics
S Sriboonchitta, HT Nguyen, O Kosheleva, V Kreinovich, TN Nguyen
Structural Changes and their Econometric Modeling 12, 191-199, 2019
Mandates: US National Science Foundation
Fast convolution and fast Fourier transform under interval and fuzzy uncertainty
G Liu, V Kreinovich
Journal of Computer and System Sciences 76 (1), 63-76, 2010
Mandates: US National Institutes of Health
Why are FGM copulas successful? A simple explanation
S Sriboonchitta, V Kreinovich
Advances in fuzzy systems 2018 (1), 5872195, 2018
Mandates: US National Science Foundation, National Natural Science Foundation of China
√(x2+ μ) is the Most Computationally Efficient Smooth Approximation to| x|: a Proof
C Ramirez, R Sanchez, V Kreinovich, M Argaez
Mandates: US National Institutes of Health
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