Multi-train: A semi-supervised heterogeneous ensemble classifier
Sign Up to like & getrecommendations! Published in 2017 at "Neurocomputing"
DOI: 10.1016/j.neucom.2017.03.063
Abstract: Many real-world machine learning tasks have very limited labeled data but a large amount of unlabeled data. To take advantage of the unlabeled data for enhancing learning performance, several semi-supervised learning techniques have been developed.… read more here.
Keywords: multi train; classifier; unlabeled data; semi supervised ... See more keywords
Spatial pseudo-labeling for semi-supervised facies classification
Sign Up to like & getrecommendations! Published in 2020 at "Journal of Petroleum Science and Engineering"
DOI: 10.1016/j.petrol.2020.107834
Abstract: Abstract For the Quantitative classification of facies is crucial to link seismic data with its corresponding lithology for the evaluation of reservoir properties. During the past decade, seismic volumes have increased to the degree that… read more here.
Keywords: spatial pseudo; pseudo; pseudo labeling; semi supervised ... See more keywords
Safe Semi-Supervised Extreme Learning Machine for EEG Signal Classification
Sign Up to like & getrecommendations! Published in 2018 at "IEEE Access"
DOI: 10.1109/access.2018.2868713
Abstract: One major challenge in the current brain–computer interface research is the accurate classification of time-varying electroencephalographic (EEG) signals. The labeled EEG samples are usually scarce, while the unlabeled samples are available in large quantities and… read more here.
Keywords: classification; semi supervised; extreme learning; unlabeled data ... See more keywords
Self-supervised On-device Federated Learning from Unlabeled Streams
Sign Up to like & getrecommendations! Published in 2022 at "IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems"
DOI: 10.1109/tcad.2023.3274956
Abstract: The ubiquity of edge devices has led to a growing amount of unlabeled data produced at the edge. Deep learning models deployed on edge devices are required to learn from these unlabeled data to continuously… read more here.
Keywords: device federated; federated learning; supervised device; self supervised ... See more keywords
Deep Fourier Ranking Quantization for Semi-Supervised Image Retrieval
Sign Up to like & getrecommendations! Published in 2022 at "IEEE Transactions on Image Processing"
DOI: 10.1109/tip.2022.3203612
Abstract: To reduce the extreme label dependence of supervised product quantization methods, the semi-supervised paradigm usually employs massive unlabeled data to assist in regularizing deep networks, thereby improving model performance. However, the existing method focuses on… read more here.
Keywords: deep fourier; semi supervised; quantization; ranking quantization ... See more keywords
Efficient Active Learning by Querying Discriminative and Representative Samples and Fully Exploiting Unlabeled Data
Sign Up to like & getrecommendations! Published in 2021 at "IEEE Transactions on Neural Networks and Learning Systems"
DOI: 10.1109/tnnls.2020.3016928
Abstract: Active learning is an important learning paradigm in machine learning and data mining, which aims to train effective classifiers with as few labeled samples as possible. Querying discriminative (informative) and representative samples are the state-of-the-art… read more here.
Keywords: discriminative representative; active learning; querying discriminative; learning ... See more keywords
Variable Selection Under Missing Values and Unlabeled Data in Semiconductor Processes
Sign Up to like & getrecommendations! Published in 2019 at "IEEE Transactions on Semiconductor Manufacturing"
DOI: 10.1109/tsm.2018.2881286
Abstract: Manufacturing semiconductor wafers involves many sequential processes, and each process has various equipment-related variables or factors, which results in high-dimensional data. However, measuring the quality of all wafers is time and cost intensive, and only… read more here.
Keywords: variable selection; semiconductor; missing values; equipment ... See more keywords
Improving Chemical Reaction Prediction with Unlabeled Data
Sign Up to like & getrecommendations! Published in 2022 at "Molecules"
DOI: 10.3390/molecules27185967
Abstract: Predicting products of organic chemical reactions is useful in chemical sciences, especially when one or more reactants are new organics. However, the performance of traditional learning models heavily relies on high-quality labeled data. In this… read more here.
Keywords: chemical reaction; prediction; unlabeled data; reaction prediction ... See more keywords