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- W2363339356 abstract "A semi-supervised learning system was proposed based on ART(adaptive resonance theory).It overcomes the limitation in the assumption in other semi-supervised learning algorithms that probabilistic distribution of data is known,and has the strong ability of learning new patterns and correcting errors because of stability and plasticity of the adaptive resonance theory.Higher adaptability of the system was advanced by setting vigilance parameters dynamically.Experimental results illustrate that the performances of the proposed system is better than the discriminant CEM(classification expectation maximization) algorithm,particularly when there are noise data and new patterns." @default.
- W2363339356 created "2016-06-24" @default.
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- W2363339356 date "2006-01-01" @default.
- W2363339356 modified "2023-09-25" @default.
- W2363339356 title "Document Classification by Semi-supervised Online Learning Based on ART" @default.
- W2363339356 hasPublicationYear "2006" @default.
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