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基于覆盖的分类算法研究进展
  • 期刊名称：计算机学报，30(8):1235-1243, 2007
  • 时间：0
  • 分类：TP181[自动化与计算机技术—控制科学与工程;自动化与计算机技术—控制理论与控制工程]
  • 作者机构：[1]中国科学院计算技术研究所智能信息处理重点实验室,北京100080
  • 相关基金：本课题得到国家自然科学基金（60435010,90604017,60675010）、国家“八六三”高技术研究发展计划项目基金（2006AA012128）、国家“九七三”重点基础研究发展规划项目基金（2003CB311004）和北京市自然科学基金（4052025）资助
  • 相关项目：网络智能化及与语义Web相关的基础理论与关键技术研究
关键词： 覆盖算法, 基于超曲面的分类方法, 极小一致集, 机器学习, covering algorithm, hyper surface classification, minimal consistent subset, machinelearning
中文摘要：

理解数据与感知数据密切相关．覆盖学习算法在低维空间往往能模拟人的视觉感知来表示数据分布．文中综述了基于覆盖的分类算法的研究进展，特别对基于超曲面的覆盖分类算法进行了详细阐述和分析，并指出了基于超曲面的分类算法进一步研究的方向．

英文摘要：

The understanding of data is highly relevant to how one senses and perceives them. The covering learning algorithms can always simulate human visual cognition to represent the data distribution in the low dimension space. The advances in the area of covering based classification algorithms are summarized. Specially,the Hyper Surface Classification is introduced and analyzed in detail. Moreover, the future research directions are pointed out.

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