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检索条件"机构=Key Laboratory for Symbolic Computation and Knowledge Engineering of Ministry of Education"
1087 条 记 录,以下是141-150 订阅
排序:
An Improved Pigeon-Inspired Optimization for Multi-focus Noisy Image Fusion
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Journal of Bionic engineering 2021年 第6期18卷 1452-1462页
作者: Yingda Lyu Yunqi Zhang Haipeng Chen Public Computer Education and Research Center Jilin UniversityChangchun130012China College of Software Jilin UniversityChangchun 130012China College of Computer Science and Technology Jilin UniversityChangchun 130012China Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education Jilin UniversityChangchun 130012China
Image fusion technology is the basis of computer vision task,but information is easily affected by noise during *** this paper,an Improved Pigeon-Inspired Optimization(IPIO)is proposed,and used for multi-focus noisy i... 详细信息
来源: 评论
SS-MAF: Semi-Supervised Echocardiographic Segmentation Using Multi-Atlas Fusion
SS-MAF: Semi-Supervised Echocardiographic Segmentation Using...
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Communication, Image and Signal Processing (CCISP), International Conference on
作者: Shuaihua Yang Caixia Zheng College of Information Science and Technology Northeast Normal University Changchun China Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education Jilin University Changchun China
Left ventricle (LV) segmentation in echocardiography is of paramount significance in cardiac function analysis. Recently, semi-supervised segmentation has garnered considerable attention due to its potential mitigatio...
来源: 评论
Troublemaker Learning for Low-Light Image Enhancement
arXiv
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arXiv 2024年
作者: Song, Yinghao Cao, Zhiyuan Xiang, Wanhong Long, Sifan Ge, Hongwei Liang, Yanchun Yang, Bo Wu, Chunguo College of Computer Science and Technology Jilin University Jilin China Key Laboratory of Symbolic Computation and Knowledge Engineering Ministry of Education Jilin University Jilin China YGSOFT INC Dalian University of Technology China
Low-light image enhancement (LLIE) restores the color and brightness of underexposed images. Supervised methods suffer from high costs in collecting low/normal-light image pairs. Unsupervised methods invest substantia... 详细信息
来源: 评论
Migration: An Efficient Explorer Operator to Guide Swarm Evolution
Migration: An Efficient Explorer Operator to Guide Swarm Evo...
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Medical Artificial Intelligence (MedAI), IEEE International Conference on
作者: Xiaosong Han Chengyang Zhou Yanchun Liang Renchu Guan Key Laboratory for Symbol Computation and Knowledge Engineering of National Education Ministry College of Computer Science and Technology Jilin University Changchun China Key Laboratory for Symbol Computation and Knowledge Engineering of National Education Ministry College of Computer Science and Technology Zhuhai Laboratory of Key Laboratory for Symbol Computation and Knowledge Engineering of Ministry of Education Jilin University Zhuhai College of Science and Technology Zhuhai China
Particle Swarm Optimization with Migration (MPSO) is proposed to solve the issue that PSO will encounter unbearable time cost problems when dealing with High-dimension, Expensive and Black-box objective function tasks...
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DeciLS-PBO: an Effective Local Search Method for Pseudo-Boolean Optimization
arXiv
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arXiv 2023年
作者: Jiang, Luyu Ouyang, Dantong Zhang, Qi Zhang, Liming College of Computer Science and Technology Jilin University Changchun130012 China Key Laboratory of Symbolic Computation and Knowledge Engineering Ministry of Education Jilin University Changchun130012 China
Local search is an effective method for solving large-scale combinatorial optimization problems, and it has made remarkable progress in recent years through several subtle mechanisms. In this paper, we found two ways ... 详细信息
来源: 评论
Robust Federated Semi-Supervised Learning for Medical Image Classification via Pseudo-Label Filtering
Robust Federated Semi-Supervised Learning for Medical Image ...
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IEEE International Conference on Bioinformatics and Biomedicine (BIBM)
作者: Ziwei Wang Shuyu Guo Mingzhu Zhu Tian Bai College of Software Jilin University Changchun China Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education Jilin University Changchun China College of Computer Science and Technology Jilin University Changchun China
Federated learning (FL) enables collaborative model training across multiple medical institutions to ensure data security. However, due to the variations in medical imaging equipment and regions at different medical i... 详细信息
来源: 评论
Uncertainty-Aware Global-View Reconstruction for Multi-View Multi-Label Feature Selection
arXiv
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arXiv 2025年
作者: Hao, Pingting Liu, Kunpeng Gao, Wanfu College of Computer Science and Technology Jilin University China Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education Jilin University China Department of Computer Science Portland State University PortlandOR97201 United States
In recent years, multi-view multi-label learning (MVML) has gained popularity due to its close resemblance to real-world scenarios. However, the challenge of selecting informative features to ensure both performance a... 详细信息
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Improved CS Algorithm and its Application in Parking Space Prediction
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Journal of Bionic engineering 2020年 第5期17卷 1075-1083页
作者: Rui Guo Xuanjing Shen Hui Kang College of Computer Science and Technology Jilin UniversityChangchun 130012China Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education Jilin UniversityChangchun 130012China
This paper simulates the cuckoo incubation process and flight path to optimize the Wavelet Neural Network(WNN)model,and proposes a parking prediction algorithm based on WNN and improved Cuckoo Search(CS)***,the initia... 详细信息
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WHO IS YOUR RIGHT MIXUP PARTNER IN POSITIVE AND UNLABELED LEARNING  10
WHO IS YOUR RIGHT MIXUP PARTNER IN POSITIVE AND UNLABELED LE...
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10th International Conference on Learning Representations, ICLR 2022
作者: Li, Changchun Li, Ximing Feng, Lei Ouyang, Jihong College of Computer Science and Technology Jilin University China College of Computer Science Chongqing University China Imperfect Information Learning Team RIKEN Center for Advanced Intelligence Project Japan Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education China
Positive and Unlabeled (PU) learning targets inducing a binary classifier from weak training datasets of positive and unlabeled instances, which arise in many real-world applications. In this paper, we propose a novel... 详细信息
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Reconsidering Feature Structure Information and Latent Space Alignment in Partial Multi-label Feature Selection
arXiv
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arXiv 2025年
作者: Pan, Hanlin Liu, Kunpeng Gao, Wanfu College of Computer Science and Technology Jilin University China Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education Jilin University China Department of Computer Science Portland State University PortlandOR97201 United States
The purpose of partial multi-label feature selection is to select the most representative feature subset, where the data comes from partial multi-label datasets that have label ambiguity issues. For label disambiguati... 详细信息
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