Currently, Manifold learning gradually becomes a hotspot in the field of machine learning and pattern recognition.
月前，流形学习渐渐成为机器学习及模式识别领域中的一个研究热点。
Since the data samples in machine learning and pattern recognition problems generally distribute in multi-modal distribution, this thesis proposed a prototype based feature ranking model.
由于模式识别、机器学习等问题的复杂性比较高，数据分布通常呈现多模态分布。
Feature selection is an important issue in the fields of machine learning and pattern recognition. The effectiveness of feature directly affects the design and performance of the classifier.
特征选择问题是机器学习和模式识别中的一个重要问题，特征的优劣直接影响分类器的设计和性能。
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