Showing 121 - 140 results of 1,665 for search 'T13 (classification)', query time: 0.06s Refine Results
  1. 121
  2. 122
  3. 123
  4. 124
  5. 125
  6. 126
  7. 127

    Multiple-Wearable-Sensor-Based Gait Classification and Analysis in Patients with Neurological Disorders by Wei-Chun Hsu, Tommy Sugiarto, Yi-Jia Lin, Fu-Chi Yang, Zheng-Yi Lin, Chi-Tien Sun, Chun-Lung Hsu, Kuan-Nien Chou

    Published 2018-10-01
    “…The placement-based classification of the shank sensor achieved 89.13% testing accuracy with the Decision Tree (DT) classifier algorithm. …”
    Get full text
    Article
  8. 128
  9. 129
  10. 130
  11. 131
  12. 132
  13. 133

    Motor Imagery EEG Classification Based on Multi-Domain Feature Rotation and Stacking Ensemble by Xianglong Zhu, Ming Meng, Zewen Yan, Zhizeng Luo

    Published 2025-01-01
    “…Finally, we employ a stacking ensemble approach, where the prediction results of base classifiers corresponding to different domain features and the set of significant features undergo linear discriminant analysis for dimensionality reduction, yielding discriminative feature integration as input for the meta-classifier for classification. Results: The proposed method achieves average classification accuracies of 92.92%, 89.13%, and 86.26% on the BCI Competition III Dataset IVa, BCI Competition IV Dataset I, and BCI Competition IV Dataset 2a, respectively. …”
    Get full text
    Article
  14. 134
  15. 135
  16. 136
  17. 137
  18. 138
  19. 139

    Tracking algorithm of Siamese network based on online target classification and adaptive template update by Zhiwang CHEN, Zhongxin ZHANG, Juan SONG, Haipeng LEI, Yong PENG

    Published 2021-08-01
    “…Aiming at the problem that tracking algorithm of Siamese network learned the embedded features of the tracked target and the object in the offline training stage, and these embedded features often lacked the target-specific context information, which made these tracking algorithms less robust, a tracking algorithm of the Siamese network based on online target classification and adaptive template update was proposed, which used SiamRPN++ as the baseline algorithm.Firstly, a cross-correlation feature map supervision module for classification was designed in the offline training phase to learn more discriminative embedded features.Secondly, an online target classification module that included an attention mechanism in the online tracking phase was designed, and the online update filter strategy in the module was used to filter out the background noise.Finally, an adaptive template update module was designed to update the target template information using the UpdateNet.The results of experiments on VOT2018 and VOT2019 datasets verify the effectiveness of the proposed algorithm, which brings 13.5% and 18.2% (EAO) improvement respectively compared with the baseline algorithm SiamRPN++.…”
    Get full text
    Article
  20. 140