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ORCIDTakéhiko Nakama
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2010 – 2019
- 2014
[j4]Takéhiko Nakama, Enrique H. Ruspini:
Combining dependent evidential bodies that share common knowledge. Int. J. Approx. Reason. 55(9): 2109-2125 (2014)- 2013
- [c12]Takéhiko Nakama:
A Mathematician's Naive Perspective on Fuzzy Sets and Fuzzy Logic. On Fuzziness (2) 2013: 459-461 - [c11]Takéhiko Nakama, Enrique H. Ruspini:
Generalizing Precisiated Natural Language: A Formal Logic as a Precisiation Language. EUSFLAT Conf. 2013 - [c10]Enrique Muñoz, Takéhiko Nakama, Enrique H. Ruspini:
Hierarchical Qualitative Descriptions of Perceptions for Robotic Environments. IJCCI 2013: 309-319 - [c9]Takéhiko Nakama, Enrique Muñoz, Enrique H. Ruspini:
Generalization and Formalization of Precisiation Language with Applications to Human-Robot Interaction. IJCCI 2013: 332-343 - [c8]Takéhiko Nakama, Enrique Muñoz, Kevin LeBlanc, Enrique H. Ruspini:
Facilitating Human-Robot Interaction: A Formal Logic for Task Description. ROBOT (2) 2013: 331-344 - 2012
- [j3]Takéhiko Nakama:
Markov chain analysis of genetic algorithms applied to fitness functions perturbed concurrently by additive and multiplicative noise. Comput. Optim. Appl. 51(2): 601-622 (2012) - [c7]Takéhiko Nakama, Michio Sugeno:
Admissibility of preferences and modeling capabilities of fuzzy integrals. FUZZ-IEEE 2012: 1-8 - [p2]Takéhiko Nakama, Enric Trillas, Itziar García-Honrado:
Axiomatic Investigation of Fuzzy Probabilities. Soft Computing in Humanities and Social Sciences 2012: 125-140 - [i1]James Allen Fill, Takéhiko Nakama:
Distributional convergence for the number of symbol comparisons used by QuickSelect. CoRR abs/1202.2599 (2012) - 2011
- [c6]Takéhiko Nakama:
Comparisons of Single- and Multiple-Hidden-Layer Neural Networks. ISNN (1) 2011: 270-279 - 2010
- [j2]James Allen Fill, Takéhiko Nakama:
Analysis of the Expected Number of Bit Comparisons Required by Quickselect. Algorithmica 58(3): 730-769 (2010) - [c5]Enric Trillas, Takéhiko Nakama, Itziar García-Honrado:
Fuzzy Probabilities: Tentative Discussions on the Mathematical Concepts. IPMU 2010: 139-148 - [c4]Takéhiko Nakama, Ana Colubi, María Asunción Lubiano:
Two-Way Analysis of Variance for Interval-Valued Data. SMPS 2010: 475-482
2000 – 2009
- 2009
- [j1]Takéhiko Nakama:
Theoretical analysis of batch and on-line training for gradient descent learning in neural networks. Neurocomputing 73(1-3): 151-159 (2009) - [c3]Takéhiko Nakama:
Markov chain analysis of genetic algorithms in a wide variety of noisy environments. GECCO 2009: 827-834 - 2008
- [c2]James Allen Fill, Takéhiko Nakama:
Analysis of the Expected Number of Bit Comparisons Required by Quickselect. ANALCO 2008: 249-256 - [c1]Takéhiko Nakama:
Theoretical analysis of genetic algorithms in noisy environments based on a Markov Model. GECCO 2008: 1001-1008 - [p1]Takéhiko Nakama:
Markov Chain Analysis of Genetic Algorithms Applied to Fitness Functions Perturbed by Multiple Sources of Additive Noise. Software Engineering, Artificial Intelligence, Networking and Parallel/Distributed Computing 2008: 123-136
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