對拉曼光譜信號分類的模式識別

郭平; 盧漢清; 杜為民

一级黄色片免费播放|中国黄色视频播放片|日本三级a|可以直接考播黄片影视免费一级毛片

留言板

尊敬的讀者、作者、審稿人, 關于本刊的投稿、審稿、編輯和出版的任何問題, 您可以本頁添加留言。我們將盡快給您答復。謝謝您的支持!

姓名

郵箱

手機號碼

標題

留言內(nèi)容

驗證碼

對拉曼光譜信號分類的模式識別

郭平, 盧漢清, 杜為民

文章導航 > 電子與信息學報 > 2002 > 24(6): 789-793

郭平, 盧漢清, 杜為民. 對拉曼光譜信號分類的模式識別[J]. 電子與信息學報, 2002, 24(6): 789-793.

引用本文:

郭平, 盧漢清, 杜為民. 對拉曼光譜信號分類的模式識別[J]. 電子與信息學報, 2002, 24(6): 789-793.

Guo Ping, Lu Hanqing, Du Weimin. Pattern recognition for the classification of Raman spectroscopy signals[J]. Journal of Electronics & Information Technology, 2002, 24(6): 789-793.

Citation:

Guo Ping, Lu Hanqing, Du Weimin. Pattern recognition for the classification of Raman spectroscopy signals[J]. Journal of Electronics & Information Technology, 2002, 24(6): 789-793.

郭平, 盧漢清, 杜為民. 對拉曼光譜信號分類的模式識別[J]. 電子與信息學報, 2002, 24(6): 789-793.

引用本文:

郭平, 盧漢清, 杜為民. 對拉曼光譜信號分類的模式識別[J]. 電子與信息學報, 2002, 24(6): 789-793.

Guo Ping, Lu Hanqing, Du Weimin. Pattern recognition for the classification of Raman spectroscopy signals[J]. Journal of Electronics & Information Technology, 2002, 24(6): 789-793.

Citation:

Guo Ping, Lu Hanqing, Du Weimin. Pattern recognition for the classification of Raman spectroscopy signals[J]. Journal of Electronics & Information Technology, 2002, 24(6): 789-793.

對拉曼光譜信號分類的模式識別

郭平^{①;盧漢清},
盧漢清,
杜為民

計量
- 文章訪問數(shù): 2271
- HTML全文瀏覽量: 153
- PDF下載量: 490
- 被引次數(shù): 0
出版歷程
- 收稿日期: 2000-08-23
- 修回日期: 2001-04-23
- 刊出日期: 2002-06-19

Pattern recognition for the classification of Raman spectroscopy signals

Guo Ping^{①;盧漢清},
Lu Hanqing,
Du Weimin

摘要

摘要: 該文提出了應用重整化的高斯分類器對拉曼光譜信號識別。把每條光譜信號看作是高維矢量空間的一個點,建立統(tǒng)計模型后采用貝葉斯定則進行概率分類。計算機實驗結(jié)果表明識別正確率可達到99.81％。
- 拉曼光譜; 模式識別; 分類; 貝葉斯定則; 重整化; 甄別分析
Abstract: This paper presents a study on application of regularized Gaussian classifier to Raman spectroscopy signals pattern recognition. Each Raman spectrum is treated as a point in high dimensional vector space. For given samples, the statistical model is used and Bayes decision is adopted to classify then according to their maximum posterior probabilities. Computer experiments show that the classification accuracy is obtained as high as 99.81%.

HTML全文

參考文獻(1)

Stefan Aeberhard, Danny Coomans and Olivier de Vel, Comparative analysis of statistical pattern recognition methods in high dimensional settings, Pattern Recognition, 1994, 27(8), 1065-1077.[2]K. Fukunaga, Introduction to Statistical Pattern Recognition, second edition, Boston, Academic Press, 1990, chapter 2. ［3］J. H. Friedman, Regularized discriminant analysis, J. of American Statistics Association, 1989,84(405), 165-175.[3]Ping Guo.[J].Michael R. Lyu, Classification for high-dimension small-sample data sets based on kullback-leibler information measure, Proc. of the Int. Conference on Artificial Intelligence (ICAl2000), Las Vegas, Nevada, USA: CSREA Press.2000,:-[4]C.M. Bishop, Neural Networks for Pattern Recognition, 1995, Oxford, Oxford University Press,1995, 80-90.

施引文獻

資源附件(0)

訪問統(tǒng)計