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武汉大学 [4]
大连理工大学 [3]
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Seasonal and trend forecasting of tourist arrivals: An adaptive multiscale ensemble learning approach
期刊论文
INTERNATIONAL JOURNAL OF TOURISM RESEARCH, 2022, 页码: 18
作者:
Xing, Guangyuan
;
Sun, Shaolong
;
Bi, Dan
;
Guo, Ju-e
;
Wang, Shouyang
收藏
  |  
浏览/下载:142/0
  |  
提交时间:2022/04/02
ensemble learning
least square support vector regression
seasonality
tourism demand forecasting
variational mode decomposition
SRSC: Selective, Robust, and Supervised Constrained Feature Representation for Image Classification
期刊论文
IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS, 2020, 卷号: 31, 期号: 10, 页码: 4290-4302
作者:
Xie, Guo-Sen
;
Zhang, Zheng
;
Liu, Li
;
Zhu, Fan
;
Zhang, Xu-Yao
收藏
  |  
浏览/下载:24/0
  |  
提交时间:2021/01/07
Training
Task analysis
Learning systems
Computational modeling
Optimization
Support vector machines
Principal component analysis
Feature learning
feature selection
least squares
subspace learning
Prediction of end-of-season tuber yield and tuber set in potatoes using in-season uav-based hyperspectral imagery and machine learning
期刊论文
Sensors (Switzerland), 2020, 卷号: 20, 期号: 18, 页码: 1-13
作者:
Sun, Chen
;
Feng, Luwei
;
Zhang, Zhou
;
Ma, Yuchi
;
Crosby, Trevor
收藏
  |  
浏览/下载:24/0
  |  
提交时间:2020/10/26
hyperspectral imaging
machine learning
tuber yield
tuber set
unmanned aerial vehicles
Application of Machine Learning Method to Quantitatively Evaluate the Droplet Size and Deposition Distribution of the UAV Spray Nozzle
期刊论文
APPLIED SCIENCES-BASEL, 2020, 卷号: 10
作者:
Guo, Han
;
Zhou, Jun
;
Liu, Fei
;
He, Yong
;
Huang, He
收藏
  |  
浏览/下载:22/0
  |  
提交时间:2020/11/26
UAV spray nozzle
spray characteristics
machine learning
quantitative modeling
Satellite-Derived Bathymetry Model in the Arctic Waters Based on Support Vector Regression
期刊论文
JOURNAL OF COASTAL RESEARCH, 2019, 页码: 294-301
作者:
Zhang, Jing-Yu
;
Zhang, Jie
;
Ma, Yi
;
Chen, An-Na
;
Cheng, Jie
收藏
  |  
浏览/下载:31/0
  |  
提交时间:2019/10/29
Satellite-derived bathymetry
support vector regression
residual interpolation
Arctic
Satellite-Derived Bathymetry Model in the Arctic Waters Based on Support Vector Regression
期刊论文
JOURNAL OF COASTAL RESEARCH, 2019, 页码: 294-301
作者:
Zhang, Jing-Yu
;
Zhang, Jie
;
Ma, Yi
;
Chen, An-Na
;
Cheng, Jie
收藏
  |  
浏览/下载:21/0
  |  
提交时间:2019/10/29
Satellite-derived bathymetry
support vector regression
residual interpolation
Arctic
Analyzing the Uncertainty of Estimating Forest Aboveground Biomass Using Optical Imagery and Spaceborne LiDAR
期刊论文
REMOTE SENSING, 2019, 卷号: 11, 期号: 6, 页码: 16
作者:
Sun, Xiaofang
;
Li, Guicai
;
Wang, Meng
;
Fan, Zemeng
收藏
  |  
浏览/下载:53/0
  |  
提交时间:2019/09/24
aboveground biomass
prediction model
number of samples
cross-validation
remotely sensed data
Rapid Detection of Pesticide Residues in Paddy Water Using Surface-Enhanced Raman Spectroscopy
期刊论文
SENSORS, 2019, 卷号: 19, 期号: 3, 页码: 10
作者:
Weng, Shizhuang
;
Zhu, Wenxiu
;
Dong, Ronglu
;
Zheng, Ling
;
Wang, Fang
收藏
  |  
浏览/下载:9/0
  |  
提交时间:2020/07/14
pesticides
SERS
paddy water
rapid detection
Comparison of LSSVR, M5RT, NF-GP, and NF-SC Models for Predictions of Hourly Wind Speed and Wind Power Based on Cross-Validation
期刊论文
Energies, 2019, 卷号: Vol.12 No.2, 页码: 329
作者:
Rana Muhammad Adnan
;
Zhongmin Liang
;
Xiaohui Yuan
;
Ozgur Kisi
;
Muhammad Akhlaq
收藏
  |  
浏览/下载:5/0
  |  
提交时间:2019/12/17
wind speed
wind power
forecasting
least square support vector regression
M5 regression tree
neuro-fuzzy system
Sotavento Galicia wind farm
A secondary-decomposition-ensemble learning paradigm for forecasting PM2.5 concentration
期刊论文
ATMOSPHERIC POLLUTION RESEARCH, 2018, 卷号: 9, 期号: 6, 页码: 989-999
作者:
Gan, Kai
;
Sun, Shaolong
;
Wang, Shouyang
;
Wei, Yunjie
收藏
  |  
浏览/下载:43/0
  |  
提交时间:2018/11/16
Secondary-decomposition-ensemble learning paradigm
Complementary ensemble empirical mode decomposition
Phase space reconstruction
Least square support vector regression
Hybrid intelligent algorithm
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