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Improvement of EMG Pattern Recognition Model Performance in Repeated Uses by Combining Feature Selection and Incremental Transfer Learning

机译：通过组合特征选择和增量转移学习改进重复用途的EMG模式识别模型性能

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期刊名称 Frontiers in Neurorobotics
作者
Qi Li; Anyuan Zhang; Zhenlan Li; Yan Wu;
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作者单位

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年(卷),期 2021(-1),-1
年度 2021
页码 -1
总页数 11
原文格式 PDF
正文语种
中图分类情报学;
关键词

机译：EMG;模式识别;特征选择;增量支持向量机;TradaBoost;
入库时间 2022-08-21 12:18:40

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专利

1. Identification of a feature selection based pattern recognition scheme for finger movement recognition from multichannel EMG signals [J] . Geethanjali Purushothaman, Raunak Vikas Australasian physical & engineering sciences in medicine . 2018,第2期

机译：识别基于特征选择的模式识别方案，用于从多通道EMG信号中识别手指运动
2. Pattern recognition and features selection for speech emotion recognition model using deep learning [J] . Kittisak Jermsittiparsert, Abdurrahman Abdurrahman, Parinya Siriattakul, International journal of speech technology . 2020,第4期

机译：使用深度学习的语音情感识别模型的模式识别和特征选择
3. EMG feature assessment for myoelectric pattern recognition and channel selection: A study with incomplete spinal cord injury [J] . LiuJ., LiX., LiG., Medical engineering & physics. . 2014,第7期

机译：用于肌电模式识别和通道选择的肌电图特征评估：脊髓不完全损伤的研究
4. Intelligent Pattern Recognition by Feature Selection through Combined Model of DWT and ANN [C] . Cheol-Ki Kim, Eui-Young Cha, Tae-Soo Chon International Symposium on Methodologies for Intelligent Systems . 2003

机译：通过DWT和ANN的组合模型选择智能模式识别
5. Algorithms for enhancing pattern separability, feature selection and incremental learning with applications to gas-sensing electronic nose systems. [D] . Polikar, Robi. 2000

机译：应用于气敏电子鼻系统的算法，用于增强模式可分离性，特征选择和增量学习。
6. EMG Feature Assessment for Myoelectric Pattern Recognition and Channel Selection: A Study with Incomplete Spinal Cord Injury [O] . Jie Liu, Xiaoyan Li, Guanglin Li, -1

机译：用于肌电模式识别和通道选择的肌电图特征评估：脊髓不完全损伤的研究
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8. Investigation of Feature Selection Criteria for Pattern Recognition Models Including the Fourier Transmission [R] . Olson, E. A. 1973

机译：包含傅里叶变换的模式识别模型特征选择准则研究

Improvement of EMG Pattern Recognition Model Performance in Repeated Uses by Combining Feature Selection and Incremental Transfer Learning

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