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北京大学 [4]
自动化研究所 [4]
大连化学物理研究所 [3]
海洋研究所 [2]
中国农业科学院 [2]
上海大学 [2]
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期刊论文 [33]
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浏览/检索结果:
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Multi-view laplacian least squares for human emotion recognition
期刊论文
NEUROCOMPUTING, 2019, 卷号: 370, 页码: 78-87
作者:
Guo, Shuai
;
Feng, Lin
;
Feng, Zhan-Bo
;
Li, Yi-Hao
;
Wang, Yang
收藏
  |  
浏览/下载:11/0
  |  
提交时间:2020/03/30
Multi-view learning
Laplacian least squares
Subspace learning
Human emotion recognition
A generalized multi-dictionary least squares framework regularized with multi-graph embeddings
期刊论文
PATTERN RECOGNITION, 2019, 卷号: 90, 页码: 1-11
作者:
Abeo, Timothy Apasiba
;
Shen, Xiang-Jun
;
Bao, Bing-Kun
;
Zha, Zheng-Jun
;
Fan, Jianping
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  |  
浏览/下载:53/0
  |  
提交时间:2019/12/16
Multi-view dimension reduction
Least squares
Multiple graphs
Feature extraction
Classification
A soil quality index for evaluation of degradation under land use and soil erosion categories in a small mountainous catchment, Iran
期刊论文
JOURNAL OF MOUNTAIN SCIENCE, 2019, 卷号: 16, 期号: 11, 页码: 2577-2590
作者:
Nosrati, Kazem
;
Collins, Adrian L.
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  |  
浏览/下载:73/0
  |  
提交时间:2020/11/05
Soil quality index
Land use
Erosion status
Soil enzyme activities
Multivariate statistical techniques
Otolith shape analysis for stock discrimination of two Collichthys genus croaker (Pieces:Sciaenidae,) from the northern Chinese coast
期刊论文
JOURNAL OF OCEANOLOGY AND LIMNOLOGY, 2018, 卷号: 36, 期号: 3, 页码: 981-989
作者:
Zhao Bo
;
Liu Jinhu
;
Song Junjie
;
Cao Liang
;
Dou Shuozeng
收藏
  |  
浏览/下载:86/0
  |  
提交时间:2019/07/15
otolith
size descriptors
elliptic Fourier coefficients (EFC)
stock structure
Collichthys lucidus
Collichthys niveatus
Otolith shape analysis for stock discrimination of two Collichthys genus croaker (Pieces: Sciaenidae,) from the northern Chinese coast
期刊论文
JOURNAL OF OCEANOLOGY AND LIMNOLOGY, 2018, 卷号: 36, 期号: 3, 页码: 981-989
作者:
Zhao Bo
;
Liu Jinhu
;
Song Junjie
;
Cao Liang
;
Dou Shuozeng
收藏
  |  
浏览/下载:26/0
  |  
提交时间:2020/01/03
otolith
size descriptors
elliptic Fourier coefficients (EFC)
stock structure
Collichthys lucidus
Collichthys niveatus
Dynamic process fault diagnosis using improved Fisher discriminant analysis - An approach towards IoT
期刊论文
COGNITIVE SYSTEMS RESEARCH, 2018, 卷号: 52, 页码: 261-266
作者:
Tian, Huifeng[1]
;
Jia, Li[2]
收藏
  |  
浏览/下载:12/0
  |  
提交时间:2019/04/22
Improved Fisher discriminant analysis
Relative error of variance
Hypothesis testing
Canonical correlation analysis
Local Discriminant Canonical Correlation Analysis for Supervised PolSAR Image Classification
期刊论文
IEEE GEOSCIENCE AND REMOTE SENSING LETTERS, 2017, 卷号: 14, 期号: 11, 页码: 2102-2106
作者:
Huang, Xiayuan
;
Zhang, Bo
;
Qiao, Hong
;
Nie, Xiangli
收藏
  |  
浏览/下载:21/0
  |  
提交时间:2018/07/30
Canonical correlation analysis (CCA)
dimensionality reduction (DR)
local discriminant embedding (LDE)
multiview feature extraction
supervised polarimetric synthetic aperture radar (PolSAR) image classification
Local Discriminant Canonical Correlation Analysis for Supervised PolSAR Image Classification
期刊论文
IEEE GEOSCIENCE AND REMOTE SENSING LETTERS, 2017, 卷号: 14, 期号: 11, 页码: 2102-2106
作者:
Huang, Xiayuan
;
Zhang, Bo
;
Qiao, Hong
;
Nie, Xiangli
收藏
  |  
浏览/下载:21/0
  |  
提交时间:2017/12/30
Canonical Correlation Analysis (Cca)
Dimensionality Reduction (Dr)
Local Discriminant Embedding (Lde)
Multiview Feature Extraction
Supervised Polarimetric Synthetic Aperture Radar (Polsar) Image Classification
Nuclear Norm-Based 2DLPP for Image Classification
期刊论文
IEEE TRANSACTIONS ON MULTIMEDIA, 2017, 卷号: 19, 期号: 11, 页码: 2391-2403
作者:
Lu, Yuwu
;
Yuan, Chun
;
Lai, Zhihui
;
Li, Xuelong
;
Wong, Wai Keung
收藏
  |  
浏览/下载:42/0
  |  
提交时间:2017/12/25
Image Classification
Preserving Projections
Robust
Two-dimensional
Response of the phytoplankton community to water quality in a local alpine glacial lake of Xinjiang Tianchi, China: potential drivers and management implications
期刊论文
ENVIRONMENTAL SCIENCE-PROCESSES & IMPACTS, 2017, 卷号: 19, 期号: 10, 页码: 1300-1311
作者:
Suriyanarayanan, Sarvajayakesavalu
;
Lu, Xiaotian
;
Song, Shuai
;
Lu, Yonglong
;
Wang, Tieyu
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  |  
浏览/下载:33/0
  |  
提交时间:2018/07/20
Air Pollution
Climate Change
Co2 Fertilization
Crop
Ozone
Risk
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