Mar 11, 2004
Multi-class Pattern Recognition based on Compression of High Dimensional Features
Technical report of IEICE. PRMU
- ,
- ,
- Volume
- 103
- Number
- 737
- First page
- 97
- Last page
- 102
- Language
- Japanese
- Publishing type
- Publisher
- The Institute of Electronics, Information and Communication Engineers
This paper proposes a framework for multi-class pattern recognition based on compression of high dimensional features. In pattern recognition, since it is generally difficult to specify features that are suitable for classification of each class in advance, it is important to take the features into consideration broadly from various viewpoints in feature extraction from input pattern. However, if high dimensional features acquired as a result are directly used for classification, various problems such as the explosion of amount of calculation will be caused. Then, high dimensional features in which many features considered to be useful in classification were included are effectively compressed by a neural network to low dimensional features. This realizes highly precise and high-speed recognition.
- Link information
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- CiNii Articles
- http://ci.nii.ac.jp/naid/110003273982
- CiNii Books
- http://ci.nii.ac.jp/ncid/AN10541106
- URL
- http://id.ndl.go.jp/bib/6928083
- ID information
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- ISSN : 0913-5685
- CiNii Articles ID : 110003273982
- CiNii Books ID : AN10541106