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- W2473006705 abstract "Feature Extraction (FE) based on Principal Component Analysis (PCA) can effectively improve classification results by reducing the interference among features. However, such a good method has not been employed in previous studies of Incremental Attribute Learning (IAL), a novel machine learning strategy, where features are gradually trained one by one in order to remove interference among features and improve classification results. This study proposed a preprocessing for neural IAL algorithm based on feature extraction with PCA. Experimental results show that this approach is not only very efficient, but also applicable for pattern classification performance improvement." @default.
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- W2473006705 date "2016-03-01" @default.
- W2473006705 modified "2023-09-23" @default.
- W2473006705 title "Neural incremental attribute learning based on principal component analysis" @default.
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- W2473006705 doi "https://doi.org/10.1109/icbda.2016.7509830" @default.
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