[1]赵梦梦,李凡长.邻域同调学习算法[J].智能系统学报,2014,9(3):336-342.[doi:10.3969/j.issn.1673-4785.201403063]
ZHAO Mengmeng,LI Fanzhang.Neighborhood homology learning algorithm[J].CAAI Transactions on Intelligent Systems,2014,9(3):336-342.[doi:10.3969/j.issn.1673-4785.201403063]
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ZHAO Mengmeng,LI Fanzhang.Neighborhood homology learning algorithm[J].CAAI Transactions on Intelligent Systems,2014,9(3):336-342.[doi:10.3969/j.issn.1673-4785.201403063]
邻域同调学习算法
《智能系统学报》[ISSN 1673-4785/CN 23-1538/TP] 卷:
9
期数:
2014年第3期
页码:
336-342
栏目:
学术论文—智能系统
出版日期:
2014-06-25
- Title:
- Neighborhood homology learning algorithm
- Keywords:
- homology learning; homology algebra; machine learning; margin partitioning; margin homology learning; neighborhood homology learning algorithm; neighborhood complex graphs; similarity
- 分类号:
- TP181
- 摘要:
- 目前已有的边缘学习算法对边缘可变的数据划分问题存在一些不足, 这些算法在分类过程中不能有效地保证数据的结构特征不变。因而文章首先通过引进同调代数中的单形划分理论, 从机器学习的角度对分类问题中的边缘划分进行研究, 提出了一种邻域同调学习算法。算法给出了图形的邻域复形的构造方法和判断2个给定图形相似性的判定标准。最后通过在USPS-ALL手写数字集数据库和MPEG7 CE图像库上与SVM、TVQ算法的对比实验验证了本算法的有效性。
- Abstract:
- At present , the existing margin learning algorithms still have some affects when attempting to solve the data partitioning problem of variable margins.These algorithms can not effectively maintain the structure feature of datas in classification.. At present, the existing margin learning algorithms still have defects when attempting to solve the data partitioning problem of variable margins. As a consequence, this paper initially proposes a neighborhood homology learning algorithm through using the monomorphic division theory in homology algebra. The neighborhood homology learning algorithm reasearchs the margin partitioning problem from the perspective of machine learning. The neighborhood homology learning algorithm includes the method of structuring the neighborhood complex, and the criterion for judging the similarity between two given graphs. Finally, this algorithm is justified through the experimental results contrasted with SVM and TVQ on an image dataset named MPEG7 CE and a database of handwritten digits named USPS-ALL.
- 参考文献/References:
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备注/Memo
收稿日期:2014-03-22。
基金项目:国家自然科学基金资助项目(61033013, 60775045)
作者简介:赵梦梦,女,1991年生,硕士研究生,主要研究方向为机器学习。
通讯作者:李凡长,男,1964年生,教授,博士生导师,中国人工智能学会理事,中国人工智能学会的机器学习专委会常务委员,机器感知与虚拟现实专委会委员,智能系统工程专委会委员,粗糙集与软计算专委会常委,中国计算机学会高级会员,中国计算机学会的理论计算机科学专委会委员,人工智能与模式识别专委会委员。主要研究方向是动态模糊逻辑和李群机器学习等,先后承担国家自然科学基金重点、面上及省级项目8项,获省级科技奖2项,发表学术论文150余篇,出版专著7部,E-mail:lfzh@suda.edu.cn。
更新日期/Last Update:
1900-01-01