Semi‐supervised multiple empirical kernel learning with pseudo empirical loss and similarity regularization
Sign Up to like & getrecommendations! Published in 2022 at "International Journal of Intelligent Systems"
DOI: 10.1002/int.22690
Abstract: Multiple empirical kernel learning (MEKL) is a scalable and efficient supervised algorithm based on labeled samples. However, there is still a huge amount of unlabeled samples in the real‐world application, which are not applicable for… read more here.
Keywords: labeled samples; empirical kernel; unlabeled samples; kernel learning ... See more keywords
Optical sectioning of unlabeled samples using bright-field microscopy
Sign Up to like & getrecommendations! Published in 2022 at "Proceedings of the National Academy of Sciences of the United States of America"
DOI: 10.1073/pnas.2122937119
Abstract: The bright-field (BF) optical microscope is a traditional bioimaging tool that has been recently tested for depth discrimination during evaluation of specimen morphology; however, existing approaches require dedicated instrumentation or extensive computer modeling. We report… read more here.
Keywords: bright field; microscopy; sectioning unlabeled; optical sectioning ... See more keywords
Learning From Reliable Unlabeled Samples for Semi-Supervised SAR ATR
Sign Up to like & getrecommendations! Published in 2022 at "IEEE Geoscience and Remote Sensing Letters"
DOI: 10.1109/lgrs.2022.3197892
Abstract: Synthetic aperture radar automatic target recognition (SAR ATR) has been suffering from the insufficient labeled samples as the annotation of SAR data is time-consuming. Thus, adding unlabeled samples into training has attracted the attention of… read more here.
Keywords: supervised sar; semi supervised; unlabeled samples; sar atr ... See more keywords
Semisupervised Hyperspectral Image Classification Using a Probabilistic Pseudo-Label Generation Framework
Sign Up to like & getrecommendations! Published in 2022 at "IEEE Transactions on Geoscience and Remote Sensing"
DOI: 10.1109/tgrs.2022.3195924
Abstract: Deep neural networks (DNNs) show impressive performance for hyperspectral image (HSI) classification when abundant labeled samples are available. The problem is that HSI sample annotation is extremely costly and the budget for this task is… read more here.
Keywords: classification; framework; pseudo; unlabeled samples ... See more keywords
Quickest Anomaly Detection in Sensor Networks With Unlabeled Samples
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DOI: 10.1109/tsp.2023.3256275
Abstract: The problem of quickest anomaly detection in networks with unlabeled samples is studied. At some unknown time, an anomaly emerges in the network and changes the data-generating distribution of some unknown sensor. The data vector… read more here.
Keywords: detection; anomaly detection; unlabeled samples; quickest anomaly ... See more keywords
Research on the Guidance of Youth Labor Education Based on the “Combination of Education and Production Labor” Program Based on the Deep Learning Model
Sign Up to like & getrecommendations! Published in 2022 at "Computational Intelligence and Neuroscience"
DOI: 10.1155/2022/2576559
Abstract: At present, there is a lack of research on Marx's idea of “combining education and productive labor” and its guiding significance for youth labor education, and no effective teaching model has been formed. In response… read more here.
Keywords: labor education; education; model; unlabeled samples ... See more keywords