System and method to enable training a machine learning network in the presence of weak or absent training exemplars

• 1. 机器学习模型的训练样本的筛选方法及其系统 [P] . 中国专利： CN111047051A . 2020-04-21
• 2. 机器学习模型的训练样本的筛选方法及其系统 [P] . 中国专利： CN110968886A . 2020-04-07
• 3. System and method to enable training a machine learning network in the presence of weak or absent training exemplars [P] . 美国专利： US8838511B2 . 2014-09-16

  机译： 在弱或缺少训练样本的情况下能够训练机器学习网络的系统和方法

• 4. System and Method to Enable Training a Machine Learning Network in the Presence of Weak or Absent Training Exemplars [P] . 美国专利： US2012084238A1 . 2012-04-05

  机译： 在弱或缺少训练样本的情况下能够训练机器学习网络的系统和方法

• 5. System and Method to Enable Training a Machine Learning Network in the Presence of Weak or Absent Training Exemplars [P] . 美国专利： US2009037351A1 . 2009-02-05

  机译： 在弱或缺少训练样本的情况下能够训练机器学习网络的系统和方法

• 1. Sampling Methods for Efficient Training of Graph Convolutional Networks:A Survey [J] . Xin Liu, Mingyu Yan, Lei Deng, 自动化学报：英文版 . 2022,第002期
• 2. Performance of statistical and machine learning-based methods for predicting biogeographical patterns of fungal productivity in forest ecosystems [J] . Albert Morera, Juan Martínez de Aragón, José Antonio Bonet, 森林生态系统：英文版 . 2021,第002期
• 3. Applications of machine learning techniques to a sensor-network-based prosthesis training system [J] . Chenn-Jung Huang, Yu-Wu Wang, Tz-Hau Huang, Applied Soft Computing . 2011,第3期
• 4. Supporting Peer Learning Networks for Case-Based Learning in Public Health: Experience of the Rocky Mountain Public Health Training Center With the ECHO Training Model [J] . Elaine Scallan, Sarah Davis, Fred Thomas, Pedagogy in health promotion. . 2017,第Suppla1期
• 5. Regularizing extreme learning machine by dual locally linear embedding manifold learning for training multi-label neural network classifiers [J] . Mohammad Rezaei-Ravari, Mahdi Eftekhari, Farid Saberi-Movahed Engineering Applications of Artificial Intelligence . 2021,第Jana期
• 6. Machine-mediated Motor Skill Training Method in Haptic-enabled Chinese Handwriting Simulation System [C] . Dangxiao WANG, Yuru ZHANG, Chong YAO, IEEE/RSJ International Conference on Intelligent Robots and Systems . 2006
• 7. E-learning effectiveness: An examination of online training methods for training end-users of new technology systems. [D] . Esch, Thomas J. 2003
• 8. Applications of machine learning techniques to a sensor-network-based prosthesis training system [O] . Chenn-Jung Huang, Yu-Wu Wang, Tz-Hau Huang, -1
• 9. Machine-mediated Motor Skill Training Method in Haptic-enabled Chinese Handwriting Simulation System [O] . Dangxiao Wang, Yuru Zhang, Chong Yao 2015

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• 2. Performance comparison of neural network training methods based on wavelet packet transform for classification of five mental tasks [J] . Vijay Khare ,Jayashree Santhosh ,Sneh Anand . 生物医学工程（英文） . 2010,第6期
• 3. Practice and Reflection on the Apprentice Training Talents “Dual Systems Four Stages of Work Integrated Learning” Education Approach [J] . Shuai Tian . 世界建筑（百图） . 2018,第2期
• 4. 神经网络训练样本选取与优化的研究 [C] . 姜晓军 ,程卫东 . 2007年全国测控、计量、仪器仪表学术年会 . 2007
• 5. 金融票据OCR系统训练样本挑选方法的研究 [A] . 张智 . 2003