Seichi Ozawa

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It is difficult to find the hidden information in the educational database system, because of the rapid increase of the student's data. The hidden information from the educational databases can be used for the various predictions like students' performance, offering different career choices to students, prediction of student's enrollment into(More)
This paper presents a new boosting algorithm called Boosting Kernel Discriminant Analysis (BKDA) in which the feature selection and the classifier training are conducted by Kernel Discriminant Analysis (KDA) and AdaBoost.M2, respectively. To reduce the dependency between classifier outputs and to speed up the learning, each classifier is trained in the(More)
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