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- W2921135519 abstract "Because intelligent systems require a huge dataset of motions and labels to learn and recognize the meanings (labels) of body motions, we consider applying active learning, in which the system asks users about the labels. We aim to implement an effective learning and question management method by considering the context in motion performance. In this paper, we use virtual reality (VR) avatars that perform motions in different contexts consisting of the tools and places used in the motion performance. Active learning was performed by using various combinations of methods for context selection, label estimation, and question type selection (i.e., open/closed). Experimental results showed that the combination of margin sampling for context selection, a naive Bayes classifier for label estimation, and the use of open questions initially and closed questions later achieved high estimation accuracy with the least number of questions." @default.
- W2921135519 created "2019-03-22" @default.
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- W2921135519 date "2018-12-01" @default.
- W2921135519 modified "2023-09-24" @default.
- W2921135519 title "Evaluation of Rapid Active Learning Method for Motion Label Learning in Variable VR Environment" @default.
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- W2921135519 doi "https://doi.org/10.1109/robio.2018.8664859" @default.
- W2921135519 hasPublicationYear "2018" @default.
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