Results 1 to 10 of about 21,198 (108)
Fractional Imputation Algorithm for Incomplete Data Based on Multi-Model Fusion [PDF]
Jisuanji gongcheng, 2023 Missing data imputation is an important step in data mining from incomplete datasets. Existing imputation algorithms cannot effectively utilize samples with high missing rates, which results in the equivalent processing of samples with different missing ...
Liangshan SHAO, Songze ZHAO
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Multi-label multi-class COVID-19 Arabic Twitter dataset with fine-grained misinformation and situational information annotations [PDF]
PeerJ Computer Science, 2022 Since the inception of the current COVID-19 pandemic, related misleading information has spread at a remarkable rate on social media, leading to serious implications for individuals and societies.
Rasha Obeidat +3 more
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Incomplete Multi-view Classification via Discriminative and Sparse Representation
Jisuanji kexue yu tansuo, 2021 Generally, the traditional multi-view learning methods assume that all samples are completed in all views. However, this assumption often fails in real applications because of limited access to data, equipment malfunc-tion, as well as occlusion and so on.
XIN Like, YANG Wanqi, YANG Ming
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Frontiers in Oncology, 2023 IntroductionDeep learning-based solutions for histological image classification have gained attention in recent years due to their potential for objective evaluation of histological images.
Liwen Jiang +5 more
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Comparative Study on the Technology Gaps in the Field of Animal Husbandry and Veterinary Genomics between China and Foreign Countries [PDF]
Nongye tushu qingbao xuebao, 2023 [Purpose/Significance] In order to explore the technological gaps in Chinese im-portant agricultural fields and predict the future trends of these gaps, this study investigates technology opportunity discovery in the embryonic and developmental stages ...
WU Lei, LI Xiaojie, DING Qian, SUN Wei, ZHOU Zhengkui
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Remote Sensing, 2020 The lack of pixel-level labeling limits the practicality of deep learning-based building semantic segmentation. Weakly supervised semantic segmentation based on image-level labeling results in incomplete object regions and missing boundary information ...
Jie Chen +4 more
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A multi-label classification method for disposing incomplete labeled data and label relevance
Dianxin kexue, 2016 Multi-label classification methods have been applied in many real-world fields，in which the labels may have strong relevance and some of them even are incomplete or missing.However，existing multi-label classification algorithms are unable to handle both ...
Lina ZHANG, Lingpeng DAI, Tai KUANG
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Updating Correlation-Enhanced Feature Learning for Multi-Label Classification
MathematicsIn the domain of multi-label classification, label correlations play a crucial role in enhancing prediction precision. However, traditional methods heavily depend on ground-truth label sets, which can be incompletely tagged due to the diverse backgrounds
Zhengjuan Zhou +4 more
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智慧农业, 2023 SignificanceThe scientific dataset of agricultural pests and diseases is the foundation for monitoring and warning of agricultural pests and diseases.
GUAN Bolun +6 more
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Journal of King Saud University: Computer and Information SciencesDespite significant advancements in multi-view multi-label learning driven by its broad applicability, real-world scenarios frequently suffer from dual incompleteness in both view and label spaces due to data acquisition uncertainties. The incompleteness
Shenrun Ding +4 more
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