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Learning to select pseudo labels: a semi-supervised method for named entity recognition Research Articles
Zhen-zhen Li, Da-wei Feng, Dong-sheng Li, Xi-cheng Lu,lizhenzhen14@nudt.edu.cn,davyfeng.c@gmail.com,dsli@nudt.edu.cn,xclu@nudt.edu.cn
Frontiers of Information Technology & Electronic Engineering 2020, Volume 21, Issue 6, Pages 809-962 doi: 10.1631/FITEE.1800743
Keywords: 命名实体识别;无标注数据;深度学习;半监督学习方法
A review on cyber security named entity recognition Review Article
Chen Gao, Xuan Zhang, Mengting Han, Hui Liu,zhxuan@ynu.edu.cn
Frontiers of Information Technology & Electronic Engineering 2021, Volume 22, Issue 9, Pages 1153-1168 doi: 10.1631/FITEE.2000286
Keywords: 命名实体识别(NER);信息抽取;网络空间安全;机器学习;深度学习
Disambiguating named entitieswith deep supervised learning via crowd labels Article
Le-kui ZHOU,Si-liang TANG,Jun XIAO,Fei WU,Yue-ting ZHUANG
Frontiers of Information Technology & Electronic Engineering 2017, Volume 18, Issue 1, Pages 97-106 doi: 10.1631/FITEE.1601835
Keywords: Named entity disambiguation Crowdsourcing Deep learning
NGAT: attention in breadth and depth exploration for semi-supervised graph representation learning Research Articles
Jianke HU, Yin ZHANG,yinzh@zju.edu.cn
Frontiers of Information Technology & Electronic Engineering 2022, Volume 23, Issue 3, Pages 409-421 doi: 10.1631/FITEE.2000657
Keywords: Graph learning Semi-supervised learning Node classification Attention
Federated unsupervised representation learning Research Article
Fengda ZHANG, Kun KUANG, Long CHEN, Zhaoyang YOU, Tao SHEN, Jun XIAO, Yin ZHANG, Chao WU, Fei WU, Yueting ZHUANG, Xiaolin LI,fdzhang@zju.edu.cn,kunkuang@zju.edu.cn
Frontiers of Information Technology & Electronic Engineering 2023, Volume 24, Issue 8, Pages 1181-1193 doi: 10.1631/FITEE.2200268
Keywords: Federated learning Unsupervised learning Representation learning Contrastive learning
Representation learning via a semi-supervised stacked distance autoencoder for image classification Research Articles
Liang Hou, Xiao-yi Luo, Zi-yang Wang, Jun Liang,jliang@zju.edu.cn
Frontiers of Information Technology & Electronic Engineering 2020, Volume 21, Issue 7, Pages 963-1118 doi: 10.1631/FITEE.1900116
Keywords: 自动编码器;图像分类;半监督学习;神经网络
Pre-training with asynchronous supervised learning for reinforcement learning based autonomous driving Research Articles
Yunpeng Wang, Kunxian Zheng, Daxin Tian, Xuting Duan, Jianshan Zhou,ypwang@buaa.edu.cn,zhengkunxian@buaa.edu.cn,dtian@buaa.edu.cn,duanxuting@buaa.edu.cn
Frontiers of Information Technology & Electronic Engineering 2021, Volume 22, Issue 5, Pages 615-766 doi: 10.1631/FITEE.1900637
Keywords: 自主驾驶;自动驾驶车辆;强化学习;监督学习
Layer-wise domain correction for unsupervised domain adaptation Article
Shuang LI, Shi-ji SONG, Cheng WU
Frontiers of Information Technology & Electronic Engineering 2018, Volume 19, Issue 1, Pages 91-103 doi: 10.1631/FITEE.1700774
Keywords: Unsupervised domain adaptation Maximum mean discrepancy Residual network Deep learning
A self-supervised method for treatment recommendation in sepsis Research Articles
Sihan Zhu, Jian Pu,jianpu@fudan.edu.cn
Frontiers of Information Technology & Electronic Engineering 2021, Volume 22, Issue 7, Pages 926-939 doi: 10.1631/FITEE.2000127
Keywords: 治疗推荐;脓毒症;自监督学习;强化学习;电子病历
Unsupervised feature selection via joint local learning and group sparse regression Regular Papers
Yue WU, Can WANG, Yue-qing ZHANG, Jia-jun BU
Frontiers of Information Technology & Electronic Engineering 2019, Volume 20, Issue 4, Pages 538-553 doi: 10.1631/FITEE.1700804
Feature selection has attracted a great deal of interest over the past decades. By selecting meaningful feature subsets, the performance of learning algorithms can be effectively improved. Because label information is expensive to obtain, unsupervised feature selection methods are more widely used than the supervised ones. The key to unsupervised feature selection is to find features that effectively reflect the underlying data distribution. However, due to the inevitable redundancies and noise in a dataset, the intrinsic data distribution is not best revealed when using all features. To address this issue, we propose a novel unsupervised feature selection algorithm via joint local learning and group sparse regression (JLLGSR). JLLGSR incorporates local learning based clustering with group sparsity regularized regression in a single formulation, and seeks features that respect both the manifold structure and group sparse structure in the data space. An iterative optimization method is developed in which the weights finally converge on the important features and the selected features are able to improve the clustering results. Experiments on multiple real-world datasets (images, voices, and web pages) demonstrate the effectiveness of JLLGSR.
Keywords: Unsupervised Local learning Group sparse regression Feature selection
Pattern Recognition With Fuzzy Central Clustering Algorithms
Zen Huanglin,Yuan Hui,Liu Xiaofang
Strategic Study of CAE 2004, Volume 6, Issue 11, Pages 33-37
Based on optimization of constrained nonlinear programming, an approach of clustering center and a fuzzy membership function of pattern classification are derived from an objective function of the constrained nonlinear programming. An unsupervised algorithm with recursive expression and a fuzzy central cluster neural network are suggested in this paper. The fuzzy central cluster neural network proposed here can realize crisp decision or fuzzy decision in pattern classification.
Keywords: fuzzy sets central cluster pattern recognition neural network
Interactive image segmentation with a regression based ensemble learning paradigm Article
Jin ZHANG, Zhao-hui TANG, Wei-hua GUI, Qing CHEN, Jin-ping LIU
Frontiers of Information Technology & Electronic Engineering 2017, Volume 18, Issue 7, Pages 1002-1020 doi: 10.1631/FITEE.1601401
Keywords: Interactive image segmentation Multivariate adaptive regression splines (MARS) Ensemble learning Thin-plate spline regression (TPSR) Semi-supervised learning Support vector regression (SVR)
Ensemble enhanced active learning mixture discriminant analysis model and its application for semi-supervised fault classification Research Article
Weijun WANG, Yun WANG, Jun WANG, Xinyun FANG, Yuchen HE
Frontiers of Information Technology & Electronic Engineering 2022, Volume 23, Issue 12, Pages 1814-1827 doi: 10.1631/FITEE.2200053
Keywords: Semi-supervised Active learning Ensemble learning Mixture discriminant analysis Fault classification
Teng Zhou, Zhen Song, Kai Sundmacher
Engineering 2019, Volume 5, Issue 6, Pages 1017-1026 doi: 10.1016/j.eng.2019.02.011
Materials development has historically been driven by human needs and desires, and this is likely to continue in the foreseeable future. The global population is expected to reach ten billion by 2050, which will promote increasingly large demands for clean and high-efficiency energy, personalized consumer products, secure food supplies, and professional healthcare. New functional materials that are made and tailored for targeted properties or behaviors will be the key to tackling this challenge. Traditionally, advanced materials are found empirically or through experimental trial-and-error approaches. As big data generated by modern experimental and computational techniques is becoming more readily available, data-driven or machine learning (ML) methods have opened new paradigms for the discovery and rational design of materials. In this review article, we provide a brief introduction on various ML methods and related software or tools. Main ideas and basic procedures for employing ML approaches in materials research are highlighted. We then summarize recent important applications of ML for the large-scale screening and optimal design of polymer and porous materials, catalytic materials, and energetic materials. Finally, concluding remarks and an outlook are provided.
Keywords: Big data Data-driven Machine learning Materials screening Materials design
Unsupervised object detection with scene-adaptive concept learning Research Articles
Shiliang Pu, Wei Zhao, Weijie Chen, Shicai Yang, Di Xie, Yunhe Pan,xiedi@hikvision.com
Frontiers of Information Technology & Electronic Engineering 2021, Volume 22, Issue 5, Pages 615-766 doi: 10.1631/FITEE.2000567
Keywords: 视觉知识;无监督视频目标检测;场景自适应学习
Title Author Date Type Operation
Learning to select pseudo labels: a semi-supervised method for named entity recognition
Zhen-zhen Li, Da-wei Feng, Dong-sheng Li, Xi-cheng Lu,lizhenzhen14@nudt.edu.cn,davyfeng.c@gmail.com,dsli@nudt.edu.cn,xclu@nudt.edu.cn
Journal Article
A review on cyber security named entity recognition
Chen Gao, Xuan Zhang, Mengting Han, Hui Liu,zhxuan@ynu.edu.cn
Journal Article
Disambiguating named entitieswith deep supervised learning via crowd labels
Le-kui ZHOU,Si-liang TANG,Jun XIAO,Fei WU,Yue-ting ZHUANG
Journal Article
NGAT: attention in breadth and depth exploration for semi-supervised graph representation learning
Jianke HU, Yin ZHANG,yinzh@zju.edu.cn
Journal Article
Federated unsupervised representation learning
Fengda ZHANG, Kun KUANG, Long CHEN, Zhaoyang YOU, Tao SHEN, Jun XIAO, Yin ZHANG, Chao WU, Fei WU, Yueting ZHUANG, Xiaolin LI,fdzhang@zju.edu.cn,kunkuang@zju.edu.cn
Journal Article
Representation learning via a semi-supervised stacked distance autoencoder for image classification
Liang Hou, Xiao-yi Luo, Zi-yang Wang, Jun Liang,jliang@zju.edu.cn
Journal Article
Pre-training with asynchronous supervised learning for reinforcement learning based autonomous driving
Yunpeng Wang, Kunxian Zheng, Daxin Tian, Xuting Duan, Jianshan Zhou,ypwang@buaa.edu.cn,zhengkunxian@buaa.edu.cn,dtian@buaa.edu.cn,duanxuting@buaa.edu.cn
Journal Article
Layer-wise domain correction for unsupervised domain adaptation
Shuang LI, Shi-ji SONG, Cheng WU
Journal Article
A self-supervised method for treatment recommendation in sepsis
Sihan Zhu, Jian Pu,jianpu@fudan.edu.cn
Journal Article
Unsupervised feature selection via joint local learning and group sparse regression
Yue WU, Can WANG, Yue-qing ZHANG, Jia-jun BU
Journal Article
Pattern Recognition With Fuzzy Central Clustering Algorithms
Zen Huanglin,Yuan Hui,Liu Xiaofang
Journal Article
Interactive image segmentation with a regression based ensemble learning paradigm
Jin ZHANG, Zhao-hui TANG, Wei-hua GUI, Qing CHEN, Jin-ping LIU
Journal Article
Ensemble enhanced active learning mixture discriminant analysis model and its application for semi-supervised fault classification
Weijun WANG, Yun WANG, Jun WANG, Xinyun FANG, Yuchen HE
Journal Article
Big Data Creates New Opportunities for Materials Research: A Review on Methods and Applications of Machine Learning for Materials Design
Teng Zhou, Zhen Song, Kai Sundmacher
Journal Article