Ryuei Murata

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The ability of generalization by random forests is higher than that by other multi-class classifiers because of the effect of bagging and feature selection. Since random forests based on ensemble learning requires a lot of decision trees to obtain high performance, it is not suitable for implementing the algorithm on the small-scale hardware such as(More)
An electromagnetic engine valve (EMV) has received a great deal of attention due to the growing importance of issues such as fuel economy and environmental protection in the automotive. In this paper we propose a new positioning control method for a prototype linear motor developed for the EMV. This linear motor has nonlinear properties due to the detent(More)
In the field of image recognition, a high-dimensional feature vector is often used to construct a classifier. This presents a problem, however, since using a large number of features can slow down training and degrade model readability. To alleviate this problem, sequential backward selection (SBS) has come to be used as a method for selecting an effective(More)
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