Takéhiko Nakama

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Markov Chain Analysis of Genetic Algorithms in a Noisy Environment

Takéhiko Nakama, 2008 .

Convergence Properties of Genetic Algorithmsin a Wide Variety of Noisy Environments

Takéhiko Nakama, 2009 .

Analysis of the Expected Number of Bit Comparisons Required by Quickselect

James Allen Fill, Takéhiko Nakama, J. A. Fill, 2007, Algorithmica.

Using genetic algorithms to find a globally optimal solution in uncertain environments with multiple sources of additive and multiplicative noise

Takéhiko Nakama, 2009 .

5 - Attentional Capture in the Spatial and Temporal Domains

S. K. Hendel, H. Egeth, A. Leber, 2001 .

Markov chain analysis of genetic algorithms applied to fitness functions perturbed concurrently by additive and multiplicative noise

Takéhiko Nakama, 2012, Comput. Optim. Appl..

Combining dependent evidential bodies that share common knowledge

Enrique H. Ruspini, Takéhiko Nakama, E. Ruspini, 2014, Int. J. Approx. Reason..

Arithmetic and distance-based approach to the statistical analysis of imprecisely valued data

Pierpaolo D'Urso, Ana Colubi, Paolo Giordani, 2013, SOCO 2013.

Comparisons of Single- and Multiple-Hidden-Layer Neural Networks

Takéhiko Nakama, Takéhiko Nakama, 2011, ISNN.

Visual interactions in the path of apparent motion

Steven Yantis, Takèhiko Nakama, S. Yantis, 1998, Nature Neuroscience.

Analysis of execution costs for QuickSelect

Takehiko Nakama, Takéhiko Nakama, 2009 .

Theoretical analysis of batch and on-line training for gradient descent learning in neural networks

Takéhiko Nakama, Takéhiko Nakama, 2009, Neurocomputing.

A Markov chain that models genetic algorithms in noisy environments

Takehiko Nakama, Takéhiko Nakama, 2009 .

Markov chain analysis of genetic algorithms in a wide variety of noisy environments

Takéhiko Nakama, Takéhiko Nakama, 2009, GECCO.

Transition and convergence properties of genetic algorithms applied to fitness functions perturbed concurrently by additive and multiplicative noise

Take Nakama, Takéhiko Nakama, 2009, 2009 IEEE Congress on Evolutionary Computation.

Theoretical analysis of genetic algorithms in noisy environments based on a Markov Model

Takéhiko Nakama, Takéhiko Nakama, 2008, GECCO '08.

Markov Chain Analysis of Genetic Algorithms Applied to Fitness Functions Perturbed by Multiple Sources of Additive Noise

Takéhiko Nakama, Takéhiko Nakama, 2008, Software Engineering, Artificial Intelligence, Networking and Parallel/Distributed Computing.

Generalization and Formalization of Precisiation Language with Applications to Human-Robot Interaction

Enrique H. Ruspini, Takéhiko Nakama, Enrique Muñoz, 2018, IJCCI.

Generalizing Precisiated Natural Language: A Formal Logic as a Precisiation Language

Enrique H. Ruspini, Takéhiko Nakama, E. Ruspini, 2013, EUSFLAT Conf..

Facilitating Human-Robot Interaction: A Formal Logic for Task Description

Kevin LeBlanc, Enrique H. Ruspini, Takéhiko Nakama, 2013, ROBOT.

Axiomatic Investigation of Fuzzy Probabilities

Enric Trillas, Takéhiko Nakama, Itziar García-Honrado, 2012, Soft Computing in Humanities and Social Sciences.