Sparse Functional Relevance Learning in Generalized Learning Vector Quantization

被引：0
|
作者：
Villmann, Thomas [1 ]
Kaestner, Marika [1 ]
机构：
[1] Univ Appl Sci Mittweida, Dep Math Nat & Comp Sci, Tech Pl 17, D-09648 Mittweida, Germany
来源：
ADVANCES IN SELF-ORGANIZING MAPS, WSOM 2011 | 2011年 / 6731卷
关键词：
functional vector quantization; relevance learning; information theory; SELECTION;
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：
We propose a functional relevance learning for learning vector quantization of functional data. The relevance profile is taken as a superposition of a set of basis functions depending on only a few parameters compared to standard relevance learning. Moreover, the sparsity of the superposition is achieved by an entropy based penalty function forcing sparsity.
引用
收藏
页码：79 / 89
页数：11
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