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Kernel-based machine learning for fast text mining in R

Author

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  • Karatzoglou, Alexandros
  • Feinerer, Ingo

Abstract

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.

Suggested Citation

  • Karatzoglou, Alexandros & Feinerer, Ingo, 2010. "Kernel-based machine learning for fast text mining in R," Computational Statistics & Data Analysis, Elsevier, vol. 54(2), pages 290-297, February.
    • Handle: RePEc:eee:csdana:v:54:y:2010:i:2:p:290-297
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    References listed on IDEAS

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    1. Feinerer, Ingo & Hornik, Kurt & Meyer, David, 2008. "Text Mining Infrastructure in R," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 25(i05).
    2. Usotskaya, N. & Ryabko, B., 2009. "Application of information-theoretic tests for the analysis of DNA sequences based on Markov chain models," Computational Statistics & Data Analysis, Elsevier, vol. 53(5), pages 1861-1872, March.
    3. Karatzoglou, Alexandros & Smola, Alexandros & Hornik, Kurt & Zeileis, Achim, 2004. "kernlab - An S4 Package for Kernel Methods in R," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 11(i09).
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    3. Grübler, Julia & Reiter, Oliver, 2021. "Characterising non-tariff trade policy," Economic Analysis and Policy, Elsevier, vol. 71(C), pages 138-163.

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