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Can dimensionality reduction through feature extraction improve classification accuracy compared to whole-brain analysis?: Using high-dimensional neuroimaging data as input for a support vector machine to distinguish alzheimer patients from healthy controls

机译：与全脑分析相比，通过特征提取进行降维是否可以提高分类准确性？：使用高维神经影像数据作为支持向量机的输入，以区分阿尔茨海默氏症患者与健康对照

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Broers Thomas;
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2. Comparing unfolded and two-dimensional discriminant analysis and support vector machines for classification of EEM data [J] . Camilo L.M. Morais, Kássio M.G. Lima Chemometrics and Intelligent Laboratory Systems . 2017,第期

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3. High-Dimensional Data Classification Based on Smooth Support Vector Machines [J] . Santi Wulan Purnami, Shofi Andari, Yuniati Dian Pertiwi Procedia Computer Science . 2015,第1期

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5. New covariance-based feature extraction methods for classification and prediction of high-dimensional data. [D] . Sofolahan, Mopelola A. 2013

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机译：使用一维朴素贝叶斯分类器进行特征选择可提高支持向量机对CDR3曲目分类的准确性
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Can dimensionality reduction through feature extraction improve classification accuracy compared to whole-brain analysis?: Using high-dimensional neuroimaging data as input for a support vector machine to distinguish alzheimer patients from healthy controls

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