SYSTEMS AND METHODS FOR INTELLIGENTLY CURATING MACHINE LEARNING TRAINING DATA AND IMPROVING MACHINE LEARNING MODEL PERFORMANCE

• 1. 用于提高经训练的机器学习模型的性能的方法 [P] . 中国专利： CN108027899A . 2018-05-11
• 2. 面向大规模机器学习系统的机器学习模型训练方法及系统 [P] . 中国专利： CN112785000A . 2021-05-11
• 3. Systems and methods for intelligently curating machine learning training data and improving machine learning model performance [P] . 美国专利： US10679100B2 . 2020.06.09
• 4. SYSTEMS AND METHODS FOR INTELLIGENTLY CURATING MACHINE LEARNING TRAINING DATA AND IMPROVING MACHINE LEARNING MODEL PERFORMANCE [P] . 美国专利： US2019294925A1 . 2019-09-26

  机译： 智能地培养机器学习训练数据并提高机器学习模型性能的系统和方法

• 5. Systems and methods for intelligently curating machine learning training data and improving machine learning model performance [P] . 美国专利： US10303978B1 . 2019-05-28

  机译： 智能管理机器学习训练数据并改善机器学习模型性能的系统和方法

• 1. 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
• 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, 中国林学（英文版） . 2021,第002期
• 3. Data Mining and Machine Learning Methods Applied to A Numerical Clinching Model [J] . Marco G?tz, Ferenc Leichsenring, Thomas Kropp, 工程与科学中的计算机建模(英文) . 2018,第012期
• 4. Extending the Classifier Algorithms in Machine Learning to Improve the Performance in Spoken Language Understanding Systems Under Deficient Training Data [J] . Sheetal Jagdale, Milind Shah Advances in Science, Technology and Engineering Systems . 2020,第6期
• 5. An Open Combinatorial Diffraction Dataset Including Consensus Human and Machine Learning Labels with Quantified Uncertainty for Training New Machine Learning Models [J] . Jason R. Hattrick-Simpers, Brian DeCost, A. Gilad Kusne, Integrating Materials and Manufacturing Innovation . 2021,第2期
• 6. Intelligent socio-economic status prediction system using machine learning models on Rajahmundry A.P., SES dataset [J] . V Balasankar, Suresh Varma Penumatsa, PanduRanga Vital Terlapu Indian Journal of Science and Technology . 2020,第37期
• 7. Use Metaheuristics to Improve the Quality of Drilling Real-Time Data for Advance Artificial Intelligent and Machine Learning Modeling. Case Study: Cleanse Hook-Load Real-Time Data [C] . Salem Al Gharbi, Moataz Ahmed, SalahEldin ElKatatny Abu Dhabi International Petroleum Exhibition Conference . 2018
• 8. Understanding and Intervening in Machine Learning Ethics: Supporting Ethical Sensitivity in Training Data Curation [D] . Boyd, Karen. 2020
• 9. Flexible Data Trimming Improves Performance of Global Machine Learning Methods in Omics-Based Personalized Oncology [O] . Victor Tkachev, Maxim Sorokin, Constantin Borisov, 2020
• 10. Extending the Classifier Algorithms in Machine Learning to Improve the Performance in Spoken Language Understanding Systems Under Deficient Training Data [O] . Sheetal Jagdale, Milind Shah 2020

• 1. Adaptability of machine learning methods and hydrological models to discharge simulations in datasparse glaciated watersheds [J] . JI Huiping ,CHEN Yaning ,FANG Gonghuan . 干旱区科学:英文版 . 2021,第6期
• 2. Research and Modelling on the E-commerce Consumer Behavior based on Intelligent Recommendation System and Machine Learning [J] . Zhang ,Haixia . International Journal of Technology Management . 2016,第007期
• 3. 建立智能数据库的机器学习系统 [C] . 杨忠祥 . 中国第八届数据库学术大会 . 1989
• 4. 基于Sentinel--2数据的低山丘陵区水稻种植面积监测--机器学习模型分类性能与可迁移性评价 [A] . 张卫春 . 2020