A Method for Handwritten Digits Classification

被引：0

作者：

Yan Su ^{[1
]}

Zhao JiuFen ^{[2
]}

Zhao JiuLing ^{[2
]}

Li JunYing ^{[2
]}

Ma HuDong ^{[2
]}

机构：

[1] Tsinghua Univ, Beijing, Peoples R China

[2] Xian High Technol Inst, Xian, Shaanxi, Peoples R China

来源：

ICNC 2008: FOURTH INTERNATIONAL CONFERENCE ON NATURAL COMPUTATION, VOL 2, PROCEEDINGS | 2008年

关键词：

D O I：

10.1109/ICNC.2008.389

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

Kernel PCA, as an unsupervised learning method, is a nonlinear extension of PCA for finding projections that give useful nonlinear descriptors of the data. In the application of handwritten digits classification, kernel based algorithms are indeed highly competitive on a variety of problems with different characteristics. In most real-world pattern analysis tasks, kernel-based can cut the correlative features and prefer discriminable, reliable, independent and optimal features to reduce the complexity of the classifier.

引用

页码：240 / +

页数：2

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