Discovering knowledge in a large organization through support vector machines

被引:0
作者
de Mesa, J. A. Gutierrez [1 ]
Martinez, L. Bengochea [1 ]
机构
[1] Univ Alcala, Dept Comp Sci, Madrid 28871, Spain
来源
COMPUTATIONAL SCIENCE - ICCS 2008, PT 3 | 2008年 / 5103卷
关键词
stemming; indexation; support vector machines; documentation and knowledge management;
D O I
暂无
中图分类号
TP31 [计算机软件];
学科分类号
081202 ; 0835 ;
摘要
Much of the information used by an organization is collected in the form of manuals, regulations, news etc. These are grouped into controlled documentary collections, which are normally digitized and accessible via a content management system. However, obtaining new knowledge from collected documents in an organization requires not only sound search and retrieval of information tools, but also the techniques to establish relationships, discover patterns and provide overall descriptions of the entire contents of the collection. This article explores the nature of knowledge and the role that occupy the documentary collections as a source of obtaining him knowledge. It also describes the collection of documents will be used along the exposure of this study and the techniques of processing information in order to obtain the desired results. This paper describes the use of computational methods, support vector machines in particular, in a large organisation for document classification.
引用
收藏
页码:349 / 357
页数:9
相关论文
共 21 条
[1]  
ALLAN J, 2001, IR289 U MASS AMH CTR
[2]  
AVELLO DG, 2005, BLINDLIGHT NUEVA TEC
[3]  
Baeza-Yates R.A., 1999, Modern Information Retrieval
[4]  
DAWSON JL, 1974, B ASS LIT LINGUISTIC, P33
[5]  
*EUR, 2006, TES EUR PRES ALF PER
[6]  
FIGUEROLA CG, J INFORM SCI, V26, P461
[7]   On the state of the art in machine learning: A personal review [J].
Flach, PA .
ARTIFICIAL INTELLIGENCE, 2001, 131 (1-2) :199-222
[8]  
FRENK, 1999, P 16 INT JOINT C ART, P668
[9]  
JOACHIMS T, 2001, LEANING CLASSIFY TEX
[10]   An empirical study of three machine learning methods for spam filtering [J].
Lai, Chih-Chin .
KNOWLEDGE-BASED SYSTEMS, 2007, 20 (03) :249-254