[1]李文娟 胡春生.基于聚类优化覆盖的集成学习方法[J].计算机技术与发展,2010,(11):51-54.
　LI Wen-juan,HU Chun-sheng.A Combined Learning Algorithm of Optimum Covering Based on Clustering[J].,2010,(11):51-54.
点击复制

基于聚类优化覆盖的集成学习方法()
分享到：

《计算机技术与发展》[ISSN:1006-6977/CN:61-1281/TN]

卷:
期数:
2010年11期
页码:
51-54
栏目:
智能、算法、系统工程
出版日期:
1900-01-01

文章信息/Info

Title:
A Combined Learning Algorithm of Optimum Covering Based on Clustering
文章编号:
1673-629X（2010）11-0051-04
作者:
李文娟 胡春生
解放军炮兵学院
Author(s):
LI Wen-juan; HU Chun-sheng
People＇s Liberation Army Artillery Institute
关键词:
聚类; 覆盖; 相似度
Keywords:
clustering;  covering;  similarity
分类号:
TP183
文献标志码:
A
摘要:
传统的覆盖方法形成的覆盖都是＂优簇＂,但是无法形成非球状的覆盖;而聚类求覆盖的方法就可以得到非球状覆盖,但是由于很难事先找到合适的相似度,所以无法求得全部＂优簇＂。文中把两者的优点结合起来并加以推广,与SVM,NaiveBayes,交叉覆盖等学习方法相结合,形成基于聚类优化覆盖的集成学习方法,这样求得的覆盖既可以是非球状覆盖,又是全＂优簇＂,优化了覆盖领域。实验证明该方法产生的覆盖不仅数量上较少,并且覆盖的准确率较高,具有一定的抗噪声能力
Abstract:
The pure clusters can be gotten by the traditional covering algorithm,though the shape of the coves is just ball alike.And the different shapes of covers can be attained if the clustering method is proposed to get covers.Combine the two to get optimum covers,and then extend to other learning algorithms such as NaiveBayes,SVM,cross-coverage,etc.It is called a combined learning algorithm of optimum covering based on clustering.Not only it can get covers of different shapes,but also get clusters of all pure.The experiment proves the algorithm generates not only fewer covers,but also of greater precision.It has some ability of anti-noise

相似文献/References:

[1]蒋璐璐 王适 王宝成 李慧敏 李鑫慧.一种改进的标记分水岭遥感图像分割方法[J].计算机技术与发展,2010,(01):36.
　JIANG Lu-lu,WANG Shi,WANG Bao-cheng,et al.Segmentation of Remote Sensing Image Based on an Improved Labeling Watershed Algorithm[J].,2010,(11):36.
[2]张甜 罗眉 孟晓红 赵宗涛.一种基于状态特征的航天发射故障诊断技术[J].计算机技术与发展,2010,(01):93.
　ZHANG Tian,LUO Mei,MENG Xiao-hong,et al.A Technology in Fault Diagnosis of Spaceflight Launch Based on State Character[J].,2010,(11):93.
[3]王会颖 章义刚.求解聚类问题的改进人工鱼群算法[J].计算机技术与发展,2010,(03):84.
　WANG Hui-ying,ZHANG Yi-gang.An Improved Artificial Fish- Swarm Algorithm of Solving Clustering Analysis Problem[J].,2010,(11):84.
[4]赵敏 倪志伟 刘斌.K—means与朴素贝叶斯在商务智能中的应用[J].计算机技术与发展,2010,(04):179.
　ZHAO Min,NI Zhi-wei,LIU Bin.Application Research of K - Means Clustering and Naive Bayesian Algorithm in Business Intelligence[J].,2010,(11):179.
[5]吴楠 胡学钢.基于聚类分区的序列模式挖掘算法研究[J].计算机技术与发展,2010,(06):109.
　WU Nan,HU Xue-gang.Research on Clustering Partition-Based Approach of Sequential Pattern Mining[J].,2010,(11):109.
[6]耿波 仲红 徐杰 闫娜娜.用关联分析法对负荷预测结果进行二次处理[J].计算机技术与发展,2008,(04):171.
　GENG Bo,ZHONG Hong,XU Jie,et al.Using Correlation Analysis to Treat Load Forecasting Results[J].,2008,(11):171.
[7]游芳 姜建国 张坤.基于二维属性的高维数据聚类算法研究[J].计算机技术与发展,2009,(05):111.
　YOU Fang,JIANG Jian-guo,ZHANG Kun.Cluster- Algorithm Studies Based on Two- Dimensional Attribute Higher - Dimension Data[J].,2009,(11):111.
[8]刘淑英 程国建 彭方.人工神经生长细胞结构网络在医疗诊断的应用[J].计算机技术与发展,2009,(05):231.
　LIU Shu-ying,CHENG Guo-jian,PENG Fang.Applications of Growing Cell Structures of Artificial Neural Network for Medical Diagnosis[J].,2009,(11):231.
[9]范新 沈闻 丁泉勋 沈洁.基于正例和未标文档的半监督分类研究[J].计算机技术与发展,2009,(06):58.
　FAN Xin,SHEN Wen,DING Quan-xun,et al.Research on Semi- Supervised Classification Based on Positive and Unlabeled Text Document[J].,2009,(11):58.
[10]王园园 倪志伟 赵裕啸 伍章俊.基于决策树的模糊聚类评价算法及其应用[J].计算机技术与发展,2009,(09):232.
　WANG Yuan-yuan,NI Zhi-wei,ZHAO Yu-xiao,et al.Fuzzy Clustering Evaluation Algorithm Based on Decision Tree and Application[J].,2009,(11):232.

备注/Memo

备注/Memo:
欧盟项目TYPES资助（types project 29001）李文娟（1985-），女，硕士研究生，研究方向为智能信息处理
更新日期/Last Update: 1900-01-01