说明：  模式识别学习综述.该论文的英文参考文献为303篇.很有可读价值.Abstract— Classical and recent results in statistical pattern recognition and learning theory are reviewed in a two-class pattern classification setting. This basic model best illustrates intuition and analysis techniques while still containing the essential features and serving as a prototype for many applications. Topics discussed include nearest neighbor, kernel, and histogram methods, Vapnik–Chervonenkis theory, and neural networks. The presentation and the large (thogh nonexhaustive) list of references is geared to provide a useful overview of this field for both specialists and nonspecialists.
(Summary of pattern recognition learning. The paper for English 303 references. Very readable value. Abstract-Classical and recent results in statistical patternrecognition and learning theory are reviewed in a two-classpattern classification setting. This basic model best illustratesintuition and analysis techniques while still containing the essentialfeatures and serving as a prototype for many applications.Topics discussed include nearest neighbor, kernel, and histogrammethods, Vapnik-Chervonenkis theory, and neural networks. Thepresentation and the large (thogh nonexhaustive) list of referencesis geared to provide a useful overview of this field for bothspecialists and nonspecialists.)