Generalized Derivative Based Kernelized Learning Vector Quantization

被引：0

作者：

Schleif, Frank-Michael ^{[1
]}

Villmann, Thomas ^{[2
]}

Hammer, Barbara ^{[1
]}

Schneider, Petra ^{[3
]}

Biehl, Michael ^{[3
]}

机构：

[1] Univ Bielefeld, Dept Techn, Univ Str 21-23, D-33615 Bielefeld, Germany

[2] Univ Appl, Faculty Math Natural & CS, D-09648 Mittweida, Germany

[3] Univ Groningen, Johann Bernoulli Inst Math & CS, NL-9700 Groningen, Netherlands

来源：

INTELLIGENT DATA ENGINEERING AND AUTOMATED LEARNING - IDEAL 2010 | 2010年 / 6283卷

关键词：

D O I：

暂无

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

We derive a novel derivative based version of kernelized Generalized Learning Vector Quantization (KGLVQ) as an effective, easy to interpret, prototype based and kernelized classifier. It is called D-KGLVQ and we provide generalization error bounds, experimental results on real world data, showing that D-KGLVQ is competitive with KGLVQ and the SVM on UCI data and additionally show that automatic parameter adaptation for the used kernels simplifies the learning.

引用

页码：21 / +

页数：2

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