Fisher subspace tree classifier based on neural networks

被引：0

作者：

Chen, DY ^{[1
]}

Lu, XD ^{[1
]}

Zhang, LM ^{[1
]}

机构：

[1] Fudan Univ, Dept Elect Engn, Shanghai 200433, Peoples R China

来源：

ADVANCES IN NEURAL NETWORKS - ISNN 2005, PT 2, PROCEEDINGS | 2005年 / 3497卷

关键词：

D O I：

暂无

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

This paper proposes a multi-neural network classification based on fisher transformation. The new method improves HDR[1] (Hierarchical discriminate regression) method proposed in 2000, which can classify the training set from coarse to fine by non-linear dynamic clustering for high-dimension data. In proposed method a fisher subspace replaces K-L subspace of HDR that simplifies the Hierarchical tree. Simulation results show that our method is better than HDR on recognition ratio and time cost.

引用

页码：14 / 19

页数：6

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