Feature Selection for Conditional Random Field Based on the Multivariate Time Series Data with Application to the Activity Recognition(Session 3a)
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概要
- 論文の詳細を見る
Recently, a study which recognize human activity by acceleration and angular speed sensor have been actively done. Service deployment of these studies expands medical, sport, security and variable fields. In recognizing human activity, suport vector machine(SVM) is considered as one of best learning machine in many one which is known now, because SVM has high recognition accuracy and needs less computation time. But, SVM has a defect which issues with recognition of the outlier and missing value. Therefore, we focused on Conditional Random Field (CRF) which recognizes the activity while maximizing likelihood of the interval. CRF has been often used in the field such as natural language processing, and the data used in CRF is limited to one-dimensional and categorical data. In this paper, we form an opinion of a method which transfers multidimensional time series data to the data which can be analyzed by CRF and evaluate feature selection.
- 日本計算機統計学会の論文
- 2011-11-11
著者
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Kamakura Toshinari
Facility Of Science And Engineering Chuo University
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Okusa Kosuke
Graduate School Of Science And Engineering Chuo University
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Tsujimura Tomoo
Graduate School of Science and Engineering, Chuo University
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Tsujimura Tomoo
Graduate School Of Science And Engineering Chuo University
関連論文
- Feature Selection for Conditional Random Field Based on the Multivariate Time Series Data with Application to the Activity Recognition (Proceedings of Joint Meeting of the Korea-Japan Conference of Computational Statistics and the 25th Symposium of Japane
- Feature Selection for Conditional Random Field Based on the Multivariate Time Series Data with Application to the Activity Recognition(Session 3a)
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