Comparative Analysis of Support Vector Machine and Nearest Boundary Vector Classifier

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 minimumdistance 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.

关键词： pattern recognition neural networks Support Vector Machine

作者: Jacek Dybala

作者单位: Institute of Automotive Engineering,Faculty of Automotive and Construction Machinery Engineering Warsaw University of Technology Narbutta 84,02-524 Warsaw,Poland

会议类型: 国际会议

会议名称: 2009 8th International Conference on Reliability,Maintainability and Safety(第八届中国国际可靠性、维修性、安全性会议)

会议地点: 成都

会议语种:英文

页码: 963-965

在线出版日期: 2009-08-24（万方平台首次上网日期，不代表论文的发表时间）