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科研机构
北京航空航天大学 [8]
大连理工大学 [4]
兰州理工大学 [3]
复旦大学上海医学院 [2]
地理科学与资源研究所 [1]
计算技术研究所 [1]
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期刊论文 [18]
会议论文 [6]
发表日期
2019 [24]
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Agronomy [1]
Forestry [1]
Meteorolog... [1]
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Predictive mapping with small field sample data using semi-supervised machine learning
期刊论文
TRANSACTIONS IN GIS, 2019, 页码: 17
作者:
Du, Fei
;
Zhu, A-Xing
;
Liu, Jing
;
Yang, Lin
收藏
  |  
浏览/下载:4/0
  |  
提交时间:2020/05/19
Deep Learning with Long Short-Term Memory Networks for Air Temperature Predictions
会议论文
Dublin, Ireland, October 17, 2019 - October 19, 2019
作者:
Li, Cong
;
Zhang, Yaonan
;
Zhao, Guohui
收藏
  |  
浏览/下载:1/0
  |  
提交时间:2020/11/15
Agricultural robots
Agriculture
Atmospheric temperature
Brain
Decision trees
Deep learning
Deep neural networks
Forecasting
Manufacture
Random forests
Agricultural productions
Air temperature
Meteorological variables
Prediction accuracy
Process temperature
Short term memory
Temperature prediction
Time-series data
A Novel Feature Incremental Learning Method for Sensor-Based Activity Recognition
期刊论文
IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING, 2019, 卷号: 31, 期号: 6, 页码: 1038-1050
作者:
Hu, Chunyu
;
Yu, Han
;
Peng, Xiaohui
;
Chen, Yiqiang
;
Gao, Chenlong
收藏
  |  
浏览/下载:63/0
  |  
提交时间:2019/08/16
Feature incremental learning
activity recognition
random forest
Variations in soil nutrient availability across Tibetan grassland from the 1980s to 2010s
期刊论文
Geoderma, 2019, 卷号: 338, 页码: 197-205
作者:
Tian, Liming
;
Zhao, Lin
;
Wu, Xiaodong
;
Hu, Guojie
;
Fang, Hongbing
收藏
  |  
浏览/下载:4/0
  |  
提交时间:2022/02/17
Climate change
Decision trees
Ecosystems
Nitrogen
Permafrost
Phosphorus
Potassium
Vegetation
Alpine grasslands
Available nutrients
Available phosphorus
Environmental factors
Nutrient availability
Permafrost degradation
Soil available nutrients
Tibetan Plateau
The diagnostic value of texture analysis in predicting WHO grades of meningiomas based on ADC maps: an attempt using decision tree and decision forest
期刊论文
EUROPEAN RADIOLOGY, 2019, 卷号: 29, 期号: 3
作者:
Lu, Yiping
;
Luan, Shihai
;
Xiong, Ji
;
Geng, Daoying
;
Yin, Bo
收藏
  |  
浏览/下载:20/0
  |  
提交时间:2019/12/05
Diffusion magnetic resonance imaging
Meningioma
Machine learning
Decision trees
Functional Site Discovery From Incomplete Training Data: A Case Study With Nucleic Acid-Binding Proteins
期刊论文
FRONTIERS IN GENETICS, 2019, 卷号: 10
作者:
Wang, Wenchuan
;
LangIois, Robert
;
Langlois, Marina
;
Genchev, Georgi Z.
;
Wang, Xiaolei
收藏
  |  
浏览/下载:133/0
  |  
提交时间:2019/12/05
machine learning
protein sequence and structural analysis
multiple-instance learning
decision trees
semi supervised learning
protein function annotation
DNA binding proteins
RNA binding proteins
Modeling human-like decision-making for inbound smart ships based on fuzzy decision trees
期刊论文
Expert Systems with Applications, 2019, 卷号: 115, 页码: 172-188
作者:
Xue, Jie
;
Wu, Chaozhong*
;
Chen, Zhijun*
;
Van Gelder, P. H. A. J. M.
;
Yan, Xinping
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  |  
浏览/下载:20/0
  |  
提交时间:2019/12/04
Fuzzy decision trees
Piloting decision
Smart ship
Classification rule
Data mining
Information entropy
Automated Detection and Localization of Myocardial Infarction With staked Sparse Autoencoder and TreeBagger
期刊论文
IEEE ACCESS, 2019, 卷号: 7, 页码: 70634-70642
作者:
Zhang, Jieshuo
;
Lin, Feng
;
Xiong, Peng
;
Du, Haiman
;
Zhang, Hong
收藏
  |  
浏览/下载:67/0
  |  
提交时间:2019/09/30
Electrocardiograph
myocardial infarction
sparse autoencoder
bagged decision tree
deep learning networks
Method for Daily Operation of Hydropower Stations With One Reservoir Based on Cluster Analysis and Decision Tree Technique [基于聚类分析和决策树的"一库多级"水电站日调度方法]
期刊论文
Zhongguo Dianji Gongcheng Xuebao/Proceedings of the Chinese Society of Electrical Engineering, 2019, 卷号: 39, 页码: 652-663
作者:
Shen, J.
;
Zhang, N.
;
Cheng, C.
;
Zhang, C.
收藏
  |  
浏览/下载:70/0
  |  
提交时间:2019/12/02
Cluster analysis
Data mining
Decision trees
Hydroelectric power
Regression analysis
Reservoirs (water)
Rivers
Trees (mathematics)
Water levels, Cluster
Cluster analysis methods
Constraint boundaries
Decision tree techniques
Decision-tree algorithm
Generation schedules
Hydropower plants
Linear regression methods, Hydroelectric power plants
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