關鍵字查詢 | 類別:期刊論文 | | 關鍵字:A Hierarchical Feature Selection

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序號 學年期 教師動態
1 108/1 資工系 張志勇 教授 期刊論文 發佈 Towards Human Activity Recognition: A Hierarchical Feature Selection Framework , [108-1] :Towards Human Activity Recognition: A Hierarchical Feature Selection Framework期刊論文Towards Human Activity Recognition: A Hierarchical Feature Selection Framework張志勇activity recognition;hierarchical model;feature selection;information infusionSensors 18(11), 3629The inherent complexity of human physical activities makes it difficult to accurately recognize activities with wearable sensors. To this end, this paper proposes a hierarchical activity recognition framework and two different feature selection methods to improve the recognition performance. Specifically, according to the characteristics of human activities, predefined activities of interest are organized into a hierarchical tree structure, where each internal node represents different groups of activities and each leaf node represents a specific activity label. Then, the proposed feature selection methods are appropriately integrated to optimize the feature space of each node. Finally, we train corresponding classifiers to disti
2 107/1 資工系 張志勇 教授 期刊論文 發佈 Towards Human Activity Recognition: A Hierarchical Feature Selection Framework , [107-1] :Towards Human Activity Recognition: A Hierarchical Feature Selection Framework期刊論文Towards Human Activity Recognition: A Hierarchical Feature Selection FrameworkAiguo Wang; Guilin Chen; Xi Wu; Li Liu; Ning An; Chih-Yung Changactivity recognition;hierarchical model;feature selection;information infusionSensors 18(11), 3629The inherent complexity of human physical activities makes it difficult to accurately recognize activities with wearable sensors. To this end, this paper proposes a hierarchical activity recognition framework and two different feature selection methods to improve the recognition performance. Specifically, according to the characteristics of human activities, predefined activities of interest are organized into a hierarchical tree structure, where each internal node represents different groups of activities and each leaf node represents a specific activity label. Then, the proposed feature selection methods are appropriately integrated to optimize the feature space
[第一頁][上頁]1[次頁][最末頁]目前在第 1 頁 / 共有 02 筆查詢結果