| 李群机器学习(LML)的学习子空间轨道生成理论及算法初探 |
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| 引用本文： | 陈凤,李凡长.李群机器学习(LML)的学习子空间轨道生成理论及算法初探[J].苏州大学学报(医学版),2007,23(1):61-66. |
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| 作者姓名： | 陈凤 李凡长 |
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| 作者单位： | 苏州大学计算机科学与技术学院,江苏苏州215006 |
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| 基金项目： | 江苏省自然科学基金;苏州大学211基金 |
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| 摘 要： | 给出了Lie群机器学习（LML）的学习子空间轨道生成格及相关的基本概念,包括李群机器学习中的样例数据集、轨道生成格理论及其算法,同时也给出了实例验证分析,与C4.5、N-Bayes算法在分类正确率上进行了比较,由此进一步证明了该理论的可行性以及算法的有效性.
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| 关 键 词： | 李群机器学习 学习子空间 轨道生成格 |
| 文章编号： | 1000-2073（2007）01-0061-06 |
| 收稿时间： | 2005-12-15 |
| 修稿时间： | 2005年12月15 |
Orbits generated theory of learning subspace and its algorithm in Lie-Group machine learning (LML) |
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| Chen Feng,Li Fanzhang.Orbits generated theory of learning subspace and its algorithm in Lie-Group machine learning (LML)[J].Journal of Suzhou University(Natural Science),2007,23(1):61-66. |
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| Authors: | Chen Feng Li Fanzhang |
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| Abstract: | The orbits generated lattice of learning subspace in Lie-group machine learning(LML) and its corresponding basic conceptions are proposed,which include sample set in Lie-group machine learning,orbits generated lattices theory and algorithm.Synchronously,this paper analyzes the example and compares its results with those of the C4.5 decision tree learning and N-Bayes algorithm,which shows that the new algorithm is much superior in validity of categorization,thus it can be believed that the theory is feasible and the algorithm is valid. |
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| Keywords: | Lie-group machine learning learning subspace orbits generated lattice |
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