宁夏大学学报（自然科学版）2011，Vol.32Issue(4)：341-345,5.DOI:64-1006/N.20110714.1802.002
面向多类学习问题的核最近表面分类方法
Kernel Nearest Surface Classification Methods for Multi-class Learning Problems
摘要
Abstract
The nearest neighbor decision rules have a good classification on nonlinear and imbalanced data sets, but have no learning process. A method called as kernel nearest surface classifier is proposed to condense the training samples with clustering, and then make decision using the nearest neighbor rule. The method is compared with such as common-used statistical classification methods through the experiments. The results show that the proposed method has many merits, such as fast decision speed and small memory space requirement, and can also solve the classification problem of the imbalance data set effectively.关键词
核最近表面分类/机器学习/近邻法/聚类Key words
kernel nearest surface classification method/ machine learning/ nearest neighbor rule/ clustering分类
信息技术与安全科学引用本文复制引用
殷士勇..面向多类学习问题的核最近表面分类方法[J].宁夏大学学报（自然科学版）,2011,32(4):341-345,5.基金项目
江苏省自然科学基金资助项目(BK2010277) （BK2010277）
