| 基于ART半监督在线学习的文档分类 |
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| 引用本文： | 徐敏,张丽萍,朱梧檟.基于ART半监督在线学习的文档分类[J].西南交通大学学报,2006,41(3):335-340. |
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| 作者姓名： | 徐敏 张丽萍 朱梧檟 |
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| 作者单位： | 1. 南京航空航天大学信息科学与技术学院,江苏,南京,210016
2. 南京航空航天大学理学院,江苏,南京,210016 |
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| 基金项目： | 973国家重点基金研究发展规划项目（01999032701）;国家自然科学基金资助项目（60575038） |
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| 摘 要： | 根据自适应谐振理论提出了半监督学习自适应谐振理论系统．在该系统中取消了一般半监督学习算法中假定已知数据概率分布的条件限制，利用自适应谐振理论的稳定性和可塑性，使其具有非常强的学习新模式和纠正错误能力．为了提高系统自适应性能力，将警戒参数设置为动态变化。实验结果表明半监督学习自适应谐振理论系统的性能优于判别式CEM算法，特别是在含有噪音和新模式数据情况下，其优势更为明显。
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| 关 键 词： | 在线学习 文档分类 自适应谐振理论 半监督学习 警戒参数 |
| 文章编号： | 0258-2724（2006）03-0335-06 |
| 收稿时间： | 2005-09-05 |
| 修稿时间： | 2005-09-05 |
Document Classification by Semi-supervised Online Learning Based on ART |
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| XU Min,ZHANG Liping,ZHU Wujia.Document Classification by Semi-supervised Online Learning Based on ART[J].Journal of Southwest Jiaotong University,2006,41(3):335-340. |
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| Authors: | XU Min ZHANG Liping ZHU Wujia |
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| Institution: | 1. College of Information Science and Tech. , Nanjing University of Aeronautics and Astronautics, NanJing 210016; China; 2. College of Science, Nanjing University of Aeronautics and Astronautics, NanJing 210016, China |
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| Abstract: | A semi-supervised learning system was proposed based on ART(adaptive resonance theory).It overcomes the limitation in the assumption in other semi-supervised learning algorithms that probabilistic distribution of data is known,and has the strong ability of learning new patterns and correcting errors because of stability and plasticity of the adaptive resonance theory.Higher adaptability of the system was advanced by setting vigilance parameters dynamically.Experimental results illustrate that the performances of the proposed system is better than the discriminant CEM(classification expectation maximization) algorithm,particularly when there are noise data and new patterns. |
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| Keywords: | on-line learning document classification ART semi-supervised learning vigilance parameter |
| 本文献已被 CNKI 维普 万方数据 等数据库收录！ |
