.png){kind=icon}
返回
Handwritten digit classification using higher order singular value decomposition
.png){kind=icon}
PRE
AI
DOI：10.1016/j.patcog.2006.08.004.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
In this paper we present two algorithms for handwritten digit classification based on the higher order singular value decomposition (HOSVD). The first algorithm uses HOSVD for construction of the class models and achieves classification results with error rate lower than 6%. The second algorithm uses the HOSVD for tensor approximation simultaneously in two modes. Classification results for the second algorithm are almost down at 5% even though the approximation reduces the original training data with more than 98% before the construction of the class models. The actual classification in the test phase for both algorithms is conducted by solving a series least squares problems. Considering computational amount for the test presented the second algorithm is twice as efficient as the first one. (c) 2006 Pattern Recognition Society. Published by Elsevier Ltd. All rights reserved.
Keyword:
handwritten digit classification
tensors
higher order singular value decomposition
tensor approximation
least squares
AI总结
对已上传原文的论文进行重点信息的提取，主要内容包括：简要概述、研究摘要、背景介绍、关键亮点、图文解析、展望与总结。
期刊
IF:
7.6
论文数:
1.3W
被引数:
4.5W
机构
暂无机构信息
引用论文
Gradient-based learning applied to document recognition基于梯度的学习在文档识别中的应用.png)
PROCEEDINGS OF THE IEEE
PROCEEDINGS OF THE IEEEIF25.9
PREAI
分享收藏Regio- and stereoselective synthesis of 1-aryl-1-thio-2-thiophosphinylethene derivatives via a radical processTetrahedron
OAAI
TetrahedronIF0
OAAI分享收藏 没有更多内容
没有更多内容