Zobrazeno 1 - 10
of 18
pro vyhledávání: '"WENCHONG HE"'
Autor:
Richa Dutt, Collin Ortals, Wenchong He, Zachary Charles Curran, Christine Angelini, Alberto Canestrelli, Zhe Jiang
Publikováno v:
Remote Sensing, Vol 16, Iss 14, p 2659 (2024)
Tidal creeks play a vital role in influencing geospatial evolution and marsh ecological communities in coastal landscapes. However, evaluating the geospatial characteristics of numerous creeks across a site and understanding their ecological relation
Externí odkaz:
https://doaj.org/article/ed25049d06494fc1a77dc2adbb04c26b
Publikováno v:
Frontiers in Big Data, Vol 4 (2021)
Spatial classification with limited observations is important in geographical applications where only a subset of sensors are deployed at certain spots or partial responses are collected in field surveys. For example, in observation-based flood inund
Externí odkaz:
https://doaj.org/article/54c6a6f88c4347c6933929d0ab1e6d09
Autor:
Zhe Jiang, Wenchong He
Publikováno v:
IEEE Transactions on Knowledge and Data Engineering. 34:2912-2920
Semi-supervised learning aims to learn prediction models from both labeled and unlabeled samples. There has been extensive research in this area. Among existing work, generative mixture models with Expectation-Maximization (EM) is a popular method du
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::90b9cf8e0ea3813855dae3a96ccd13c1
https://doi.org/10.1109/tkde.2020.3019038
https://doi.org/10.1109/tkde.2020.3019038
Autor:
Lei Liu, Wenchong He, Jun Zhu, Kui Deng, Huiwen Tan, Liangcheng Xiang, Xuelian Yuan, Qi Li, Menglan Huang, Yingkun Guo, Yongna Yao, Xiaohong Li
Publikováno v:
European Journal of Pediatrics.
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::472c5d7b097be78ae5464af6e11a17f7
https://doi.org/10.1007/s00431-023-04932-2
https://doi.org/10.1007/s00431-023-04932-2
Publikováno v:
ACM Transactions on Intelligent Systems and Technology. 13:1-22
Given earth imagery with spectral features on a terrain surface, this paper studies surface segmentation based on both explanatory features and surface topology. The problem is important in many spatial and spatiotemporal applications such as flood e
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::e5375ddd3ef53a1c792db28bc721d1db
https://doi.org/10.1145/3481043
https://doi.org/10.1145/3481043
Publikováno v:
Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining.
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::14ba6500367ad0456232cffa504acf2b
https://doi.org/10.1145/3534678.3539410
https://doi.org/10.1145/3534678.3539410
Publikováno v:
AAAI
Spatial classification with limited feature observations has been a challenging problem in machine learning. The problem exists in applications where only a subset of sensors are deployed at certain regions or partial responses are collected in field
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::f51f32e4fb0d35dde0ba83b41951629d
https://doi.org/10.1609/aaai.v34i01.5436
https://doi.org/10.1609/aaai.v34i01.5436
Publikováno v:
KDD
This paper studies weakly supervised learning on spatial raster data based on imperfect vector training labels. Given raster feature imagery and imperfect (weak) vector labels with location registration errors, our goal is to learn a deep learning mo
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::9c2a4dd1a7f25feef2b0dc3c0fbf5375
https://doi.org/10.1145/3447548.3467301
https://doi.org/10.1145/3447548.3467301
Autor:
Zongyou, Xu, Zhenmi, Liu, Liyong, Lu, Weibin, Liao, Chenyu, Yang, Zhongxin, Duan, Qian, Zhou, Wenchong, He, En, Zhang, Ningxiu, Li, Ke, Ju
Publikováno v:
Environmental pollution (Barking, Essex : 1987). 293
The effects of air pollution on adolescents need further consideration. Although there is evidence that maternal exposure to air pollution may affect the cognitive function of offspring, relevant studies remain limited and inconsistent, with a lack o
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=pmid________::64c49314c1889a98b571cfcb2709562a
https://pubmed.ncbi.nlm.nih.gov/34808309
https://pubmed.ncbi.nlm.nih.gov/34808309
Publikováno v:
Frontiers in Big Data, Vol 4 (2021)
Frontiers in Big Data
Frontiers in Big Data
Spatial classification with limited observations is important in geographical applications where only a subset of sensors are deployed at certain spots or partial responses are collected in field surveys. For example, in observation-based flood inund
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::b95e13892f1132093a65007bcf007d47
https://www.frontiersin.org/articles/10.3389/fdata.2021.707951/full
https://www.frontiersin.org/articles/10.3389/fdata.2021.707951/full