Learning safe multi-label prediction for weakly labeled data
Sign Up to like & getrecommendations! Published in 2017 at "Machine Learning"
DOI: 10.1007/s10994-017-5675-z
Abstract: In this paper we study multi-label learning with weakly labeled data, i.e., labels of training examples are incomplete, which commonly occurs in real applications, e.g., image classification, document categorization. This setting includes, e.g., (i) semi-supervised… read more here.
Keywords: label; weakly labeled; multi label; label learning ... See more keywords
A study on semi-supervised learning in enhancing performance of AHU unseen fault detection with limited labeled data
Sign Up to like & getrecommendations! Published in 2021 at "Sustainable Cities and Society"
DOI: 10.1016/j.scs.2021.102874
Abstract: Abstract The fault detection and diagnosis (FDD) of air handling units (AHUs) serves as a major task in building operation management and energy savings. Data-driven classification methods have gained increasing popularities considering their flexibilities and… read more here.
Keywords: supervised learning; fault detection; labeled data; semi supervised ... See more keywords
A novel transfer-learning method based on selective normalization for fault diagnosis with limited labeled data
Sign Up to like & getrecommendations! Published in 2021 at "Measurement Science and Technology"
DOI: 10.1088/1361-6501/ac03e5
Abstract: The application of deep learning to fault diagnosis has made encouraging progress in recent years. However, it is hard to obtain sufficient labeled data to ensure the performance of diagnostic models, due to complex and… read more here.
Keywords: fault; fault diagnosis; normalization; novel transfer ... See more keywords
Model Reuse in Machine Learning for Author Name Disambiguation: An Exploration of Transfer Learning
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DOI: 10.1109/access.2020.3031112
Abstract: Machine learning for author name disambiguation is usually conducted on the training and test subsets of labeled data created for a specific task. As a result, disambiguation models learned on heterogeneous labeled data are often… read more here.
Keywords: name disambiguation; author name; labeled data; disambiguation ... See more keywords
Weighted Pseudo Labeled Data and Mutual Learning for Semi-Supervised Classification
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DOI: 10.1109/access.2021.3063176
Abstract: In this article, a semi-supervised classification algorithm that is based on weighted pseudo labeled data and mutual learning is proposed. The purpose of our method is to improve the classification performance of semi-supervised learning models… read more here.
Keywords: pseudo; labeled data; semi supervised; pseudo labeled ... See more keywords
SemiPFL: Personalized Semi-Supervised Federated Learning Framework for Edge Intelligence
Sign Up to like & getrecommendations! Published in 2022 at "IEEE Internet of Things Journal"
DOI: 10.1109/jiot.2022.3233599
Abstract: Recent advances in wearable devices and Internet of Things (IoT) have led to massive growth in sensor data generated in edge devices. Labeling such massive data for classification tasks has proven to be challenging. In… read more here.
Keywords: edge users; data sets; federated learning; semipfl ... See more keywords
Specific Emitter Identification via Contrastive Learning
Sign Up to like & getrecommendations! Published in 2023 at "IEEE Communications Letters"
DOI: 10.1109/lcomm.2023.3247900
Abstract: Specific emitter identification (SEI) methods via deep learning have shown significant progress in accuracy recently. However, these methods require a large amount of the labeled data. In this letter, contrastive learning is introduced to cope… read more here.
Keywords: contrastive learning; loss; specific emitter; emitter identification ... See more keywords
Detecting Fake News With Weak Social Supervision
Sign Up to like & getrecommendations! Published in 2021 at "IEEE Intelligent Systems"
DOI: 10.1109/mis.2020.2997781
Abstract: Limited labeled data are becoming one of the largest bottlenecks for supervised learning systems. This is especially the case for many real-world tasks, where large-scale labeled examples are either too expensive to acquire or unavailable… read more here.
Keywords: supervision; weak social; social supervision; fake news ... See more keywords
Unsupervised Domain Adaptation With Global and Local Graph Neural Networks Under Limited Supervision and Its Application to Disaster Response
Sign Up to like & getrecommendations! Published in 2023 at "IEEE Transactions on Computational Social Systems"
DOI: 10.1109/tcss.2022.3159109
Abstract: Identification and categorization of social media posts generated during disasters are crucial to reduce the suffering of the affected people. However, the lack of labeled data is a significant bottleneck in learning an effective categorization… read more here.
Keywords: domain adaptation; unsupervised domain; disaster; graph neural ... See more keywords
Interinstance and Intratemporal Self-Supervised Learning With Few Labeled Data for Fault Diagnosis
Sign Up to like & getrecommendations! Published in 2023 at "IEEE Transactions on Industrial Informatics"
DOI: 10.1109/tii.2022.3183601
Abstract: Recent researches on intelligent fault diagnosis algorithms can achieve great progress. However, considering the practical scenarios, the amount of labeled data is insufficient in face of the difficulty of data annotation, which would raise the… read more here.
Keywords: fault diagnosis; supervised learning; interinstance intratemporal; self supervised ... See more keywords
Dense Prediction and Local Fusion of Superpixels: A Framework for Breast Anatomy Segmentation in Ultrasound Image With Scarce Data
Sign Up to like & getrecommendations! Published in 2021 at "IEEE Transactions on Instrumentation and Measurement"
DOI: 10.1109/tim.2021.3088421
Abstract: Segmentation of the breast ultrasound (BUS) image is an important step for subsequent assessment and diagnosis of breast lesions. Recently, Deep-learning-based methods have achieved satisfactory performance in many computer vision tasks, especially in medical image… read more here.
Keywords: labeled data; anatomy; dense prediction; image ... See more keywords