Comparative Analysis of Support Vector Machine and Nearest Boundary Vector Classifier

被引：1
|
作者：
Dybala, Jacek [1 ]
机构：
[1] Warsaw Univ Technol, Inst Automot Engn, Fac Automot & Construct Machinery Engn, PL-02524 Warsaw, Poland
来源：
PROCEEDINGS OF 2009 8TH INTERNATIONAL CONFERENCE ON RELIABILITY, MAINTAINABILITY AND SAFETY, VOLS I AND II: HIGHLY RELIABLE, EASY TO MAINTAIN AND READY TO SUPPORT | 2009年
关键词：
pattern recognition; neural networks; Support Vector Machine; COUNTERPROPAGATION NETWORKS;
D O I：
10.1109/ICRMS.2009.5269976
中图分类号：
V [航空、航天];
学科分类号：
08 ; 0825 ;
摘要：
The paper will present the original NBV (Nearest Boundary Vector) classifier whose structure has been inspired by the structure of CP (Counter Propagation) neural network, which uses the methods applied in the minimum-distance classification while in its operation drawn on the idea of functioning of SVM (Support Vector Machines) classifiers. The classification algorithm which is used by it relies on the original concept of a set of Boundary Vectors. It is characterized by the possibility of creation of various shapes of decision-making regions and it enables effective multi-class recognition. Recognition efficiency of NBV classifier will be confronted with efficiency of SVM classifiers.
引用
收藏
页码：963 / 965
页数：3
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