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Semi-supervised Sequence Learning [PDF]
, 2015 We present two approaches that use unlabeled data to improve sequence learning with recurrent networks. The first approach is to predict what comes next in a sequence, which is a conventional language model in natural language processing. The second approach is to use a sequence autoencoder, which reads the input sequence into a vector and predicts the
Andrew M. Dai, Quoc V. Le
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Active semi-supervised learning for biological data classification.
PLoS ONE, 2020 Due to datasets have continuously grown, efforts have been performed in the attempt to solve the problem related to the large amount of unlabeled data in disproportion to the scarcity of labeled data.
Guilherme Camargo +2 more
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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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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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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 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 Board, Leonard Pitt
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Human Semi‐Supervised Learning [PDF]
Topics in Cognitive Science, 2013 AbstractMost empirical work in human categorization has studied learning in either fully supervised or fully unsupervised scenarios. Most real‐world learning scenarios, however, are semi‐supervised: Learners receive a great deal of unlabeled information from the world, coupled with occasional experiences in which items are directly labeled by a ...
Bryan R, Gibson +2 more
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