METHOD AND APPARATUS FOR IMPROVING THE LEARNING PERFORMANCE OF MACHINE LEARNING MODELS ACCORDING TO THE CHARACTERISTICS OF CROWDSOURCING BASED ON PROJECTS

• 1. 基于数据隐私保护的机器学习模型特征筛选方法及装置 [P] . 中国专利： CN111079939B . 2021.04.20
• 2. 基于数据隐私保护的机器学习模型特征筛选方法及装置 [P] . 中国专利： CN111062487B . 2021.04.20
• 3. METHOD AND APPARATUS FOR DETERMINING WHETHER TO INTRODUCE MACHINE LEARNING MODELS FOR LABELING TASKS ACCORDING CHARACTERISTICS OF CROWDSOURCING BASED ON PROJECTS [P] . KR102321007B1 . 2021-11-03

  机译： 基于项目的众包的特征来确定是否引入用于标记任务的机器学习模型的方法和装置

• 4. Method and apparatus for generating supervised learning model based on unsupervised learning and method and apparatus for analyzing unsupervised learning model using the supervised learning model [P] . KR102273867B1 . 2021-07-06

  机译： 用于使用监督学习模型分析无监督学习模型的无监督学习和装置生成监督学习模型的方法和装置

• 5. SYSTEMS AND METHODS FOR INTELLIGENTLY CURATING MACHINE LEARNING TRAINING DATA AND IMPROVING MACHINE LEARNING MODEL PERFORMANCE [P] . 美国专利： US2020272855A1 . 2020-08-27

  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

• 1. Production performance forecasting method based on multivariate time series and vector autoregressive machine learning model for waterflooding reservoirs [J] . ZHANG Rui, JIA Hu 石油勘探与开发:英文版 . 2021,第001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. 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
• 4. A Novel feature reduction method to improve the performance of Machine Learning model [C] . Seyedehnafiseh Mirniaharikandehei, Morteza Heidari, Gopichandh Danala, Conference on Medical Imaging: Computer-Aided Diagnosis . 2021
• 5. Improving Cost and Schedule Performance on Large Energy Infrastructure Deployment Projects: Establishment of Best Practices for Risk Management and Organizational Learning based on Nuclear Power Generation Project Assessments. [D] . Talabi, Sola M. 2013
• 6. Flexible Data Trimming Improves Performance of Global Machine Learning Methods in Omics-Based Personalized Oncology [O] . Victor Tkachev, Maxim Sorokin, Constantin Borisov, 2020
• 7. Effectiveness of Project-based Learning Models to Improve Learning Outcomes and Learning Activities of Students in Innovative Learning [O] . Susanti ., J Susilowibowo, H Tantri Hardini 2019

• 1. Production performance forecasting method based on multivariate time series and vector autoregressive machine learning model for waterflooding reservoirs [J] . ZHANG Rui ,JIA Hu . 石油勘探与开发 . 2021,第001期
• 2. Methodology to develop machine learning algorithms to improve performance in gastrointestinal endoscopy [J] . Thomas de Lange ,Pal Halvorsen ,Michael Riegler . 世界胃肠病学杂志：英文版 . 2018,第45期
• 3. 采用机器学习模型的傣语分词方法 [C] . XIN ZhenQiu ,辛振球 ,YANG Jian . 第十三届全国人机语音通讯学术会议 . 2015
• 4. 基于机器学习模型与众包的知识融合方法研究 [A] . 李春华 . 2017