Local Gravitation Clustering-Based Semisupervised Online Sequential Extreme Learning Machine
نویسندگان
چکیده
Due to the limited number of labeled samples, semisupervised learning often leads a considerable empirical distribution mismatch between samples and unlabeled samples. To this end, paper proposes novel algorithm named Local Gravitation-based Semisupervised Online Sequential Extreme Learning Machine (LGS-OSELM), follows from easy difficult. Each sample is formulated as an object with mass associated local gravitation generated its neighbors. The similarity measurable by measures (centrality CE coordination CO). First, LGS-OSELM uses learn initialization model implementing ELM. Second, high confidence level that are pseudo label. Then, these utilized iterate neural network OS-ELM. proposed approach ultimately realizes effective all through successive iterating networks. We implement experiments on several standard benchmark data sets verify performance LGS-OSELM, which demonstrates our outperforms state-of-the-art methods in terms accuracy.
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ژورنال
عنوان ژورنال: Security and Communication Networks
سال: 2022
ISSN: ['1939-0122', '1939-0114']
DOI: https://doi.org/10.1155/2022/1735573