發信人: GzLi (笑梨), 信區: DataMining
標  題: Scaling Kernel-Based Systems to Large Data Sets
發信站: 南京大學小百合站 (Thu Jul  4 12:47:17 2002), 站內信件

篇名： Scaling Kernel-Based Systems to Large Data Sets 
刊名： Data Mining and Knowledge Discovery 
ISSN： 1384-5810 
卷期： 5 卷 3 期 出版日期： 200107  
頁碼： 從 197 頁到 211 頁共 15 頁 
作者： Tresp Volker   Siemens AG, Corporate Technology, Otto-Hahn-Ring 6,
 81730 München, Germany. volker.tresp@mchp.siemens.de
 
 
文摘： 
In the form of the support vector machine and Gaussian processes, kernel-
based systems are currently very popular approaches to supervised learning
. Unfortunately, the computational load for training kernel-based systems
 increases drastically with the size of the training data set, such that 
these systems are not ideal candidates for applications with large data sets
. Nevertheless, research in this direction is very active. In this paper,
 I review some of the current approaches toward scaling kernel-based systems
 to large data sets.
 

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