The relevance vector machine technique for channel equalization application

被引：43

作者：

Chen, S ^{[1
]}

Gunn, SR ^{[1
]}

Harris, CJ ^{[1
]}

机构：

[1] Univ Southampton, Dept Elect & Comp Sci, Southampton SO17 1BJ, Hants, England

来源：

IEEE TRANSACTIONS ON NEURAL NETWORKS | 2001年 / 12卷 / 06期

关键词：

Bayesian classification; equalization; relevance vector machines (RVMs); support vector machines (SVMs);

D O I：

10.1109/72.963792

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

The recently introduced relevance vector machine (RVM) technique is applied to communication channel equalization. It is demonstrated that the RVM equalizer can closely match the optimal performance of the Bayesian equalizer, with a much sparser kernel representation than that is achievable by the state-of-art support vector machine (SVM) technique.

引用

页码：1529 / 1532

页数：4

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