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A survey on semi-supervised learning [PDF]
Machine Learning, 2019 AbstractSemi-supervised learning is the branch of machine learning concerned with using labelled as well as unlabelled data to perform certain learning tasks. Conceptually situated between supervised and unsupervised learning, it permits harnessing the large amounts of unlabelled data available in many use cases in combination with typically smaller ...
Jesper Van Engelen +2 more
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A Survey on Deep Semi-Supervised Learning
IEEE Transactions on Knowledge and Data Engineering, 2023 Deep semi-supervised learning is a fast-growing field with a range of practical applications. This paper provides a comprehensive survey on both fundamentals and recent advances in deep semi-supervised learning methods from perspectives of model design and unsupervised loss functions.
Zenglin Xu, Irwin KING, Zixing Song
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SemiBoost: Boosting for Semi-Supervised Learning [PDF]
IEEE Transactions on Pattern Analysis and Machine Intelligence, 2009 Semi-supervised learning has attracted a significant amount of attention in pattern recognition and machine learning. Most previous studies have focused on designing special algorithms to effectively exploit the unlabeled data in conjunction with labeled data. Our goal is to improve the classification accuracy of any given supervised learning algorithm
Anil Jain, Rong Jin
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Survey of Multi-label Classification Based on Supervised and Semi-supervised Learning [PDF]
Jisuanji kexue, 2022 Most of the traditional multi-label classification algorithms use supervised learning,but in real life,there are many unlabeled data.Manual tagging of all required data is costly.Semi-supervised learning algorithms can work with a large amount of ...
WU Hong-xin, HAN Meng, CHEN Zhi-qiang, ZHANG Xi-long, LI Mu-hang
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Classification Uncertainty Minimization-based Semi-supervised Ensemble Learning Algorithm [PDF]
Jisuanji kexue, 2023 Semi-supervised ensemble learning(SSEL) is a combinatorial paradigm by fusing semi-supervised learning and ensemble learning together,which improves the diversity of ensemble learning by introducing unlabeled samples and at the same time solves the ...
HE Yulin, ZHU Penghui, HUANG Zhexue, Fournier-Viger PHILIPPE
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OBJECT DETECTION USING SEMI SUPERVISED LEARNING METHODS
ICTACT Journal on Soft Computing, 2022 Object detection is used to identify objects in real time using some deep learning algorithms. In this work, wheat plant data set around the world is collected to study the wheat heads.
Shymala Gowri Selvaganapathy +3 more
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An Improved Algorithm of Drift Compensation for Olfactory Sensors
Applied Sciences, 2022 This research mainly studies the semi-supervised learning algorithm of different domain data in machine olfaction, also known as sensor drift compensation algorithm.
Siyu Lu +6 more
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Semi-supervised Learning on Graphs Using Adversarial Training with Generated Sample [PDF]
Jisuanji kexue yu tansuo, 2023 Given a graph composed of a small number of labeled nodes and a large number of unlabeled nodes, semi-supervised learning on graphs aims to assign labels for the unlabeled nodes.
WANG Cong, WANG Jie, LIU Quanming, LIANG Jiye
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Semi-supervised Object Detection with Sequential Three-way Decision [PDF]
Jisuanji kexue, 2023 The need for large scale data in deep learning and the complexity of object detection annotation task promote the deve-lopment of semi-supervised object detection.In recent years,semi-supervised object detection has achieved many excellent results ...
SONG Faxing, MIAO Duoqian, ZHANG Hongyun
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Semi-supervised learning [PDF]
Machine Learning, 1989 The distribution-independent model of (supervised) concept learning due to Valiant (1984) is extended to that of semi-supervised learning (ss-learning), in which a collection of disjoint concepts is to be simultaneously learned with only partial information concerning concept membership available to the learning algorithm.
Raymond A. Board, Leonard Pitt
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