Kernel-based machine learning for fast text mining in R

被引：24

作者：

Karatzoglou, Alexandros ^{[1
]}

Feinerer, Ingo ^{[2
]}

机构：

[1] INSA Rouen, LITIS, F-76801 St Etienne, France

[2] Vienna Univ Technol, Inst Informat Syst, Database & Artificial Intelligence Grp, Vienna, Austria

来源：

COMPUTATIONAL STATISTICS & DATA ANALYSIS | 2010年 / 54卷 / 02期

关键词：

D O I：

10.1016/j.csda.2009.09.023

中图分类号：

TP39 [计算机的应用];

学科分类号：

081203 ; 0835 ;

摘要：

Recent advances in the field of kernel-based machine learning methods allow fast processing of text using string kernels utilizing suffix arrays. kernlab provides both kernel methods' infrastructure and a large collection of already implemented algorithms and includes an implementation of suffix-array-based string kernels. Along with the use of the text mining infrastructure provided by tm these packages provide R with functionality in processing, visualizing and grouping large collections of text data using kernel methods. The emphasis is on the performance of various types of string kernels at these tasks. (C) 2009 Elsevier B.V. All rights reserved.

引用

页码：290 / 297

页数：8

共 21 条

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