Unsupervised structure discovery for biodiversity information

The project presented concerns with improving the access to biodiversity information in legacy formats. The majority of biodiversity information, for example, floras or faunas, is still in legacy format. To mobilize these information resources, a variety of techniques have been used to 1) fit the bi...

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Bibliographic Details
Published in:	International Conference on Digital Libraries: Proceedings of the 6th ACM/IEEE-CS joint conference on Digital libraries; 11-15 June 2006 p. 382
Main Authors:	Cui, Hong, McCourt, Richard M., Feist, Monique
Format:	Conference Proceeding
Language:	English
Published:	New York, NY, USA ACM 11.06.2006 IEEE
Series:	ACM Conferences
Subjects:	Applied computing > Physical sciences and engineering > Mathematics and statistics Biodiversity Buildings Computing methodologies > Symbolic and algebraic manipulation > Computer algebra systems Data mining document structure Information resources Machine learning North America Prototypes Relational databases Tagging unsupervised machine learning XML unsupervised machine learning document structure
ISBN:	1595933549, 9781595933546
Online Access:	Get full text
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