Text categorization with support vector machines: learning with many relevant features
T. Joachims.
Proceedings of ECML-98, 10th European Conference on Machine Learning, 1398, Seite 137--142. Chemnitz, DE, Springer Verlag, Heidelberg, DE, (1998)

This paper explores the user of Support Vector machines (SVMs) for learning text classifiers from examples. It analyzes the particular properties of learning with text data and identifies, why SVMs are appropriate for this task. Empirical results support the theoretical findings. SVMs achieve substantial improvements over the currently best performing methods and they behave robustly over a variety of different learning tasks. Furthermore, they are fully automatic, eliminating the need for manuar parameter tuning.

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@inproceedings{joachims1998text,
  abstract = {This paper explores the user of Support Vector machines (SVMs) for learning text classifiers from examples. It analyzes the particular properties of learning with text data and identifies, why SVMs are appropriate for this task. Empirical results support the theoretical findings. SVMs achieve substantial improvements over the currently best performing methods and they behave robustly over a variety of different learning tasks. Furthermore, they are fully automatic, eliminating the need for manuar parameter tuning.},
  added-at = {2011-02-04T16:09:51.000+0100},
  address = {Chemnitz, DE},
  author = {Joachims, Thorsten},
  biburl = {https://puma.uni-kassel.de/bibtex/2d31522a42571f0adb834c5956540716e/benz},
  booktitle = {Proceedings of {ECML}-98, 10th European Conference on Machine Learning},
  editor = {N{\'{e}}dellec, Claire and Rouveirol, C{\'{e}}line},
  interhash = {997f731cfc4fdb02cb32eb88c4fab2e9},
  intrahash = {d31522a42571f0adb834c5956540716e},
  keywords = {studienarbeit eventually_useful},
  lastdatemodified = {2005-08-06},
  lastname = {Joachims},
  number = 1398,
  own = {own},
  pages = {137--142},
  pdf = {joachims98.pdf},
  publisher = {Springer Verlag, Heidelberg, DE},
  read = {notread},
  timestamp = {2011-02-04T16:09:51.000+0100},
  title = {Text categorization with support vector machines: learning with many relevant features},
  url = {joachims98.ps},
  year = 1998
}

%0 Conference Paper
%1 joachims1998text
%A Joachims, Thorsten
%B Proceedings of ECML-98, 10th European Conference on Machine Learning
%C Chemnitz, DE
%D 1998
%E Nédellec, Claire
%E Rouveirol, Céline
%I Springer Verlag, Heidelberg, DE
%K studienarbeit eventually_useful
%N 1398
%P 137--142
%T Text categorization with support vector machines: learning with many relevant features
%U joachims98.ps
%X This paper explores the user of Support Vector machines (SVMs) for learning text classifiers from examples. It analyzes the particular properties of learning with text data and identifies, why SVMs are appropriate for this task. Empirical results support the theoretical findings. SVMs achieve substantial improvements over the currently best performing methods and they behave robustly over a variety of different learning tasks. Furthermore, they are fully automatic, eliminating the need for manuar parameter tuning.

PUMA

Text categorization with support vector machines: learning with many relevant features
T. Joachims.
Proceedings of ECML-98, 10th European Conference on Machine Learning, 1398, Seite 137--142. Chemnitz, DE, Springer Verlag, Heidelberg, DE, (1998)

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Text categorization with support vector machines: learning with many relevant featuresT. Joachims. Proceedings of ECML-98, 10th European Conference on Machine Learning, 1398, Seite 137--142. Chemnitz, DE, Springer Verlag, Heidelberg, DE, (1998)

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Text categorization with support vector machines: learning with many relevant features
T. Joachims.
Proceedings of ECML-98, 10th European Conference on Machine Learning, 1398, Seite 137--142. Chemnitz, DE, Springer Verlag, Heidelberg, DE, (1998)