Are herb-pairs of traditional Chinese medicine distinguishable from others? Pattern analysis and artificial intelligence classification study of traditionally defined herbal properties

10.1016/j.jep.2006.11.037

Saved in:

Bibliographic Details
Main Authors:	Ung, C.Y., Li, H., Cao, Z.W., Li, Y.X., Chen, Y.Z.
Other Authors:	PHARMACY
Format:	Article
Published:	2014
Subjects:	Artificial intelligent method Herb pair Herbal medicine Herbal property Support vector machine Traditional Chinese medicine
Online Access:	http://scholarbank.nus.edu.sg/handle/10635/105678
Tags:	Add Tag No Tags, Be the first to tag this record!
Institution:	National University of Singapore

id	sg-nus-scholar.10635-105678
record_format	dspace
spelling	sg-nus-scholar.10635-1056782024-11-14T09:04:29Z Are herb-pairs of traditional Chinese medicine distinguishable from others? Pattern analysis and artificial intelligence classification study of traditionally defined herbal properties Ung, C.Y. Li, H. Cao, Z.W. Li, Y.X. Chen, Y.Z. PHARMACY Artificial intelligent method Herb pair Herbal medicine Herbal property Support vector machine Traditional Chinese medicine 10.1016/j.jep.2006.11.037 Journal of Ethnopharmacology 111 2 371-377 JOETD 2014-10-29T01:48:58Z 2014-10-29T01:48:58Z 2007-05-04 Article Ung, C.Y., Li, H., Cao, Z.W., Li, Y.X., Chen, Y.Z. (2007-05-04). Are herb-pairs of traditional Chinese medicine distinguishable from others? Pattern analysis and artificial intelligence classification study of traditionally defined herbal properties. Journal of Ethnopharmacology 111 (2) : 371-377. ScholarBank@NUS Repository. https://doi.org/10.1016/j.jep.2006.11.037 03788741 http://scholarbank.nus.edu.sg/handle/10635/105678 000246166400023 Scopus
institution	National University of Singapore
building	NUS Library
continent	Asia
country	Singapore Singapore
content_provider	NUS Library
collection	ScholarBank@NUS
topic	Artificial intelligent method Herb pair Herbal medicine Herbal property Support vector machine Traditional Chinese medicine
spellingShingle	Artificial intelligent method Herb pair Herbal medicine Herbal property Support vector machine Traditional Chinese medicine Ung, C.Y. Li, H. Cao, Z.W. Li, Y.X. Chen, Y.Z. Are herb-pairs of traditional Chinese medicine distinguishable from others? Pattern analysis and artificial intelligence classification study of traditionally defined herbal properties
description	10.1016/j.jep.2006.11.037
author2	PHARMACY
author_facet	PHARMACY Ung, C.Y. Li, H. Cao, Z.W. Li, Y.X. Chen, Y.Z.
format	Article
author	Ung, C.Y. Li, H. Cao, Z.W. Li, Y.X. Chen, Y.Z.
author_sort	Ung, C.Y.
title	Are herb-pairs of traditional Chinese medicine distinguishable from others? Pattern analysis and artificial intelligence classification study of traditionally defined herbal properties
title_short	Are herb-pairs of traditional Chinese medicine distinguishable from others? Pattern analysis and artificial intelligence classification study of traditionally defined herbal properties
title_full	Are herb-pairs of traditional Chinese medicine distinguishable from others? Pattern analysis and artificial intelligence classification study of traditionally defined herbal properties
title_fullStr	Are herb-pairs of traditional Chinese medicine distinguishable from others? Pattern analysis and artificial intelligence classification study of traditionally defined herbal properties
title_full_unstemmed	Are herb-pairs of traditional Chinese medicine distinguishable from others? Pattern analysis and artificial intelligence classification study of traditionally defined herbal properties
title_sort	are herb-pairs of traditional chinese medicine distinguishable from others? pattern analysis and artificial intelligence classification study of traditionally defined herbal properties
publishDate	2014
url	http://scholarbank.nus.edu.sg/handle/10635/105678
_version_	1821232786757910528

Are herb-pairs of traditional Chinese medicine distinguishable from others? Pattern analysis and artificial intelligence classification study of traditionally defined herbal properties

Similar Items