Titlebook: Advances in Natural Computation; First International Lipo Wang,Ke Chen,Yew Soon Ong Conference proceedings 2005 Springer-Verlag Berlin Hei

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https://doi.org/10.1057/978-1-137-59442-6r discriminatory information. In the second approach, nonlinear features are extracted using KPCA+CCA which is equivalent to KFDA in nature. The experimental results upon ORL face database indicate that the proposed PCA/KPCA+CCA significantly outperform the traditional Fisherface method.

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https://doi.org/10.1007/978-3-031-45289-5l PCA and LDA approaches, the size of the image covariance matrices using new approaches are much smaller. As a result, those new approaches have three important advantages over traditional ones. First, it is easier to evaluate the covariance matrix accurately. Second, less time is required to deter

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Monitoring of Tool Wear Using Feature Vector Selection and Linear RegressionOverview:

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Principal Component Neural Networks Based Intrusion Feature Extraction and Detection Using SVMOverview:

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GA-Driven LDA in KPCA Space for Facial Expression RecognitionOverview:

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A New ART Neural Networks for Remote Sensing Image ClassificationOverview:

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Modified Color Co-occurrence Matrix for Image RetrievalOverview: