Dec 1, 2022 · NaNG-ST outperforms 7 popular semi-supervised self-taught approaches in terms of classification accuracy, mean F-measure and required running time.
Intensive experiments on real-world data sets prove that NaNG-ST outperforms 7 popular semi-supervised self-taught approaches in terms of classification ...
NaNG-ST: A natural neighborhood graph-based self-training ...
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Intensive experiments on real-world data sets prove that NaNG-ST outperforms 7 popular semi-supervised self-taught approaches in terms of classification ...
This paper introduces a novel self-training hierarchical prototype-based approach for semi-supervised classification. The proposed approach firstly ...
2024. Add to Library. Alert. NaNG-ST: A natural neighborhood graph-based self-training method for semi-supervised classification · Junnan Li. Computer Science.
NaNG-ST constructs a natural neighborhood graph [37] on labeled and unlabeled training data. Then, they regard unlabeled samples connected with a labeled sample ...
Apr 13, 2023 · Semi-supervised learning is a learning pattern that can utilize labeled data and unlabeled data to train deep neural networks.
Missing: NaNG- ST:
NaNG-ST: A natural neighborhood graph-based self-training method for semi-supervised classification · NeurocomputingPub Date: 2022-09-20. Semi-supervised ...
BPSO-SLM: a binary particle swarm optimization-based self-labeled ...
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NaNG-ST: A natural neighborhood graph-based self-training method for semi-supervised classification · Junnan Li. Computer Science. Neurocomputing. 2022. 6 ...
Jul 20, 2023 · NaNG-ST constructs a natural neighborhood graph [37] on labeled and unlabeled training data. Then, they regard unlabeled samples connected with ...