Marginal median SOM for document organization and retrieval

被引:13
作者
Georgakis, A [1 ]
Kotropoulos, C [1 ]
Xafopoulos, A [1 ]
Pitas, I [1 ]
机构
[1] Aristotle Univ Thessaloniki, Dept Informat, Artificial Intelligence & Informat Anal Lab, GR-54124 Thessaloniki, Greece
关键词
self-organizing maps; order statistics; marginal median;
D O I
10.1016/j.neunet.2003.08.008
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
The self-organizing map algorithm has been used successfully in document organization. We now propose using the same algorithm for document retrieval. Moreover, we test the performance of the self-organizing map by replacing the linear Least Mean Squares adaptation rule with the marginal median. We present two implementations of the latter variant of the self-organizing map by either quantizing the real valued feature vectors to integer valued ones or not. Experiments performed using both implementations demonstrate a superior performance against the self-organizing map based method in terms of the number of training iterations needed so that the mean square error (i.e. the average distortion) drops to the e(-1) = 36.788% of its initial value. Furthermore, the performance of a document organization and retrieval system employing the self-organizing map architecture and its variant is assessed using the average recall-precision curves evaluated on two corpora; the first comprises of manually selected web pages over the Internet having touristic content and the second one is the Reuters-21578, Distribution 1.0. (C) 2004 Elsevier Ltd. All rights reserved.
引用
收藏
页码:365 / 377
页数:13
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