[1]章品正,徐琴珍,王征,等.一种新的支持向量机树学习模型[J].东南大学学报(自然科学版),2008,38(2):335-339.[doi:10.3969/j.issn.1001-0505.2008.02.030]
 Zhang Pinzheng,Xu Qinzhen,Wang Zheng,et al.Novel support vector machine tree learning model[J].Journal of Southeast University (Natural Science Edition),2008,38(2):335-339.[doi:10.3969/j.issn.1001-0505.2008.02.030]
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一种新的支持向量机树学习模型()
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《东南大学学报(自然科学版)》[ISSN:1001-0505/CN:32-1178/N]

卷:
38
期数:
2008年第2期
页码:
335-339
栏目:
计算机科学与工程
出版日期:
2008-03-20

文章信息/Info

Title:
Novel support vector machine tree learning model
作者:
章品正1 徐琴珍2 王征1 舒华忠1
1 东南大学计算机科学与工程学院, 南京 210096; 2 东南大学信息科学与工程学院, 南京 210096
Author(s):
Zhang Pinzheng1 Xu Qinzhen2 Wang Zheng1 Shu Huazhong1
1 School of Computer Science and Engineering, Southeast University, Nanjing 210096,China
2 School of Information Science and Engineering, Southeast University, Nanjing 210096,China
关键词:
混淆交叉 支持向量机树 有监督局部线性嵌入
Keywords:
confusion cross support vector machine tree supervised locally linear embedding
分类号:
TP301.6
DOI:
10.3969/j.issn.1001-0505.2008.02.030
摘要:
针对特征空间维数较高时,混淆交叉支持向量机树中间节点的学习结果可能包含冗余特征信息的情况,考虑各维特征之间的相互关系以及各数据点之间的相互关系对数据的分类影响,提出一种基于有监督局部线性嵌入的支持向量机树学习模型.考虑每个中间节点上需要不同的特征信息进行局部决策,分别对每个中间节点(包括根节点)上的样例进行有监督局部线性嵌入学习.实验以手写阿拉伯数字识别问题为例验证和分析了模型的结构和分类识别性能,与其他学习模型的对比结果表明,该模型能在有监督局部线性嵌入学习的基础上,以更精简的结构获得与其他学习模型可比的识别精确率.
Abstract:
The problem of redundant feature information contained in internal nodes of confusion-crossed support vector machine tree(CSVMT)for classification in high dimensional feature space is addressed. A supervised locally linear embedding based CSVMT is proposed to involve the information between different features and correlation between data points in classification phase. Since local decision in each internal node requires different feature information, the proposed approach performs supervised locally linear embedding learning in each internal node on current assigned subset. The performance of the model is experimentally demonstrated with optical recognition of handwritten digits. The comparison results illuminate that the proposed learning model can achieve competitive recognition accuracy with more condensed structure than other compared models.

参考文献/References:

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备注/Memo

备注/Memo:
作者简介: 章品正(1976—),男,博士,讲师,luckzpz@seu.edu.cn.
基金项目: 国家自然科学基金资助项目(60702029).
引文格式: 章品正,徐琴珍,王征,等.一种新的支持向量机树学习模型[J].东南大学学报:自然科学版,2008,38(2):335-339.
更新日期/Last Update: 2008-03-20