[1]王晓明,王士同.平均邻近间隔支撑向量机[J].智能系统学报,2010,5(4):313-319.
WANG Xiao-ming,WANG Shi-tong.Using average neighborhood margin with support vector machines[J].CAAI Transactions on Intelligent Systems,2010,5(4):313-319.
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WANG Xiao-ming,WANG Shi-tong.Using average neighborhood margin with support vector machines[J].CAAI Transactions on Intelligent Systems,2010,5(4):313-319.
平均邻近间隔支撑向量机
《智能系统学报》[ISSN 1673-4785/CN 23-1538/TP] 卷:
5
期数:
2010年第4期
页码:
313-319
栏目:
学术论文—人工智能基础
出版日期:
2010-08-25
- Title:
- Using average neighborhood margin with support vector machines
- 文章编号:
- 1673-4785(2010)04-0313-07
- 分类号:
- TP181
- 文献标志码:
- A
- 摘要:
- 近年来，支撑向量机（SVMs）作为一种学习方法已经在机器学习和模式识别的研究领域中得到了成功的运用，然而传统SVMs缺少考虑数据的局部信息.文中将邻近间隔的基本思想引入到SVMs中，提出了平均邻近间隔支撑向量机（ANMSVMs）.ANMSVMs继承了传统SVMs的优点，同时又充分利用了数据的局部信息，从而实现了泛化能力的进一步提高.人造数据和真实数据集的实验结果验证了该方法的有效性，并且相对于传统SVMs体现出了更强的泛化能力.
- Abstract:
- The support vector machine (SVM) is a learning method used successfully in the fields of machine learning and pattern recognition. However, traditional SVMs have not taken local information, or the environment of the data, into full consideration. To provide this context, an average neighborhood margin support vector machine (ANMSVM) was formulated by introducing the basic theories of neighborhood margins into SVMs. This method inherits the characteristics of traditional SVMs, yet also fully considers the local information surrounding the data, and thus shows better learning performance. Experimental results on artificial and real datasets indicated the effectiveness of an ANMSVM.
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备注/Memo
收稿日期：2009-09-13.
基金项目：国家自然科学基金资助项目(60704047)；国家“863”计划资助项目(2007AA1Z158,2006AA10Z313).
通信作者：王士同.E-mail:wxwangst@yahoo.com.cn.
作者简介：
王晓明，男，1977年生，博士研究生，主要研究方向为机器学习、模式识别、图像处理.
?王士同，男，1964年生，教授，博士性导师，主要研究方向为模式识别、模糊系统、生物信息学，先后10多次留学英国、日本和香港地区,发表学术论文数10篇.
更新日期/Last Update:
2010-09-20