Relational Extensions of Learning Vector Quantization

被引：0

作者：

Hammer, Barbara ^{[1
]}

Schleif, Frank-Michael ^{[1
]}

Zhu, Xibin ^{[1
]}

机构：

[1] Univ Bielefeld, CITEC Ctr Excellence, D-33615 Bielefeld, Germany

来源：

NEURAL INFORMATION PROCESSING, PT II | 2011年 / 7063卷

关键词：

LVQ; GLVQ; Soft LVQ; Dissimilarity data; Relational data; CLASSIFICATION; ALGORITHMS; TIME;

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：

Prototype-based models offer an intuitive interface to given data sets by means of an inspection of the model prototypes. Supervised classification can be achieved by popular techniques such as learning vector quantization (LVQ) and extensions derived from cost functions such as generalized LVQ (GLVQ) and robust soft LVQ (RSLVQ). These methods, however, are restricted to Euclidean vectors and they cannot be used if data are characterized by a general dissimilarity matrix. In this approach, we propose relational extensions of GLVQ and RSLVQ which can directly be applied to general possibly non-Euclidean data sets characterized by a symmetric dissimilarity matrix.

引用

页码：481 / 489

页数：9

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