高级检索   注册
SEMI OpenIR  > 中国科学院半导体研究所(2009年前)  > 期刊论文

题名: Geometrical learning, descriptive geometry, and biomimetic pattern recognition
作者: Wang, SJ;  Lai, JL
发表日期: 2005
摘要: Studies on learning problems from geometry perspective have attracted an ever increasing attention in machine learning, leaded by achievements on information geometry. This paper proposes a different geometrical learning from the perspective of high-dimensional descriptive geometry. Geometrical properties of high-dimensional structures underlying a set of samples are learned via successive projections from the higher dimension to the lower dimension until two-dimensional Euclidean plane, under guidance of the established properties and theorems in high-dimensional descriptive geometry. Specifically, we introduce a hyper sausage like geometry shape for learning samples and provides a geometrical learning algorithm for specifying the hyper sausage shapes, which is then applied to biomimetic pattern recognition. Experimental results are presented to show that the proposed approach outperforms three types of support vector machines with either a three degree polynomial kernel or a radial basis function kernel, especially in the cases of high-dimensional samples of a finite size. (c) 2005 Elsevier B.V. All rights reserved.
KOS主题词: information geometry
刊名: NEUROCOMPUTING
专题: 中国科学院半导体研究所(2009年前)_期刊论文

条目包含的文件

文件 大小格式
2569.pdf419KbAdobe PDF 联系获取全文


许可声明:条目相关作品遵循知识共享协议(Creative Commons)。


推荐引用方式:
Wang, SJ; Lai, JL .Geometrical learning, descriptive geometry, and biomimetic pattern recognition ,NEUROCOMPUTING,AUG 2005,67(0):9-28
个性服务
 推荐该条目
 保存到收藏夹
 查看访问统计
 Endnote导出
Google Scholar
 Google Scholar中相似的文章
 [Wang, SJ]的文章
 [Lai, JL]的文章
CSDL跨库检索
 CSDL跨库检索中相似的文章
 [Wang, SJ]的文章
 [Lai, JL]的文章
Scirus search
 Scirus中相似的文章
Social Bookmarking
  Add to CiteULike  Add to Connotea  Add to Del.icio.us  Add to Digg  Add to Reddit 
所有评论 (0)
暂无评论

除非特别说明,本系统中所有内容都受版权保护,并保留所有权利。

 

 

Valid XHTML 1.0! 版权所有 © 2007-2012  中国科学院半导体研究所  -反馈
系统开发与技术支持:中国科学院国家科学图书馆兰州分馆(信息系统部)
本系统基于 MIT 和 Hewlett-Packard 的 DSpace 软件开发