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Chiba, Japan

Keiai University is a private university in the city of Inage-ku, Chiba, Chiba Prefecture, Japan, established in 1966. The predecessor of the school was founded in 1921. The university has attached junior college, high schools and kindergarten. Wikipedia.


Wada R.,Keiai University
Theory and Decision | Year: 2010

This article provides an experimental analysis of attitude toward imprecise and variable information. Imprecise information is provided in the form of a set of possible probability values, such that it is virtually impossible for the subjects to guess or estimate, which one in the set is true or more likely to be true. We investigate how geometric features of such information pieces affect choices. We find that the subjects care about more features than the pairs of best-case and worst-case, which is a counter-evidence to the well-known models, maximin and α-maximin. We find that presence of nonextreme points in the set affects choice, which suggests that attitude toward imprecision is 'nonlinear.' We also obtain an observation, though not significant, that information pieces have a complementarity that may not be explained by the Bayesian view. © 2008 Springer Science+Business Media, LLC. Source


Takahashi K.,Keiai University | Taki H.,Hosei University | Tanabe S.,Waseda University | Li W.,Tokyo Institute of Technology
KEOD 2014 - Proceedings of the International Conference on Knowledge Engineering and Ontology Development | Year: 2014

We develop a new automatic coding system with a three-grade confidence level corresponding to each of the national/international standard code sets for answers to open-ended questions regarding to respondent's occupation and industry in social surveys including a national census. The "occupation and industry coding" is a necessary task for statistical processing. However, this task requires a great deal of labor and time-consuming. In addition, inconsistent results occur if the coders are not experts of coding. In formal research, various automatic coding systems have been developed, which are incomplete and generally unfriendly to a non-developer user. Our new system assigns three candidate codes to an answer for coders by SVMs (Support Vector Machines), and attaches a three-grade confidence level to the first-ranked predicted code by using classification scores to support a manual check of the results. The system is now open to the public through the Website of the Social Science Japan Data Archive (SSJDA). After the submitted data file which followed the specified format is approved, the users can obtain files of codes for up to four kinds with a three-grade confidence level. In this paper, we describe our system and evaluate it. Copyright © 2014 SCITEPRESS - Science and Technology Publications All rights reserved. Source


Taguchi I.,Keiai University | Sugai Y.,Chiba University
World Academy of Science, Engineering and Technology | Year: 2011

This paper proposes an efficient learning method for the layered neural networks based on the selection of training data and input characteristics of an output layer unit. Comparing to recent neural networks; pulse neural networks, quantum neuro computation, etc, the multilayer network is widely used due to its simple structure. When learning objects are complicated, the problems, such as unsuccessful learning or a significant time required in learning, remain unsolved. Focusing on the input data during the learning stage, we undertook an experiment to identify the data that makes large errors and interferes with the learning process. Our method devides the learning process into several stages. In general, input characteristics to an output layer unit show oscillation during learning process for complicated problems. The multi-stage learning method proposes by the authors for the function approximation problems of classifying learning data in a phased manner, focusing on their learnabilities prior to learning in the multi layered neural network, and demonstrates validity of the multi-stage learning method. Specifically, this paper verifies by computer experiments that both of learning accuracy and learning time are improved of the BP method as a learning rule of the multi-stage learning method. In learning, oscillatory phenomena of a learning curve serve an important role in learning performance. The authors also discuss the occurrence mechanisms of oscillatory phenomena in learning. Furthermore, the authors discuss the reasons that errors of some data remain large value even after learning, observing behaviors during learning. Source


Shiota S.,University of Shizuoka | Abe M.,Keiai University
Proceedings of the International Conference on e-Learning 2015, E-LEARNING 2015 - Part of the Multi Conference on Computer Science and Information Systems 2015 | Year: 2015

Having classes with "fun" incorporated into their design is crucial for learners. Students can learn from classes that combine learning with fun. In this study, we developed a program for university students in a teacher training course that aimed to teach ways of incorporating gamification into class design. Source


Taguchi I.,Keiai University | Sugai Y.,Chiba University
Electronics and Communications in Japan | Year: 2012

This paper proposes an efficient learning method for a layered neural network based on the selection of training data and the input characteristics of an output layer unit. Compared to recent neural networks, pulse neural networks, and quantum neuro computation, the multilayer neural network is widely used due to its simple structure. When learning objects are complicated, problems such as unsuccessful learning or a significant time required in learning remain unsolved. The aims of this paper are to suggest solutions for these problems and to reduce the total learning time. The total learning time means the total computational time required to learn certain objects, including adjusting parameter values and restarting learning from the beginning. Focusing on the input data during the learning stage, we undertook an experiment to identify the data that create large errors and interfere with the learning process. Our method divides the learning process into several stages. In general, the input characteristics to an output layer unit show oscillation during the learning process for complicated problems. Focusing on the oscillatory characteristics, it is determined whether the learning will move on to the next stage or the learning will restart from the beginning. Computational experiments suggest that the proposed method has the capability for higher learning performance and needs less learning time compared with the conventional method. © 2012 Wiley Periodicals , Inc. Source

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