svm.bib

@ARTICLE{burges98tutorial,
  AUTHOR = {Chris Burges},
  TITLE = {A Tutorial on Support Vector Machines for Pattern Recognition},
  JOURNAL = {Data Mining and Knowledge Discovery},
  VOLUME = {2},
  NUMBER = {2},
  YEAR = {1998},
  PAGES = {121--167}
}

@ARTICLE{collobert01,
  AUTHOR = {Ronan Collobert and Samy Bengio},
  TITLE = {Support vector machines for large-scale regression problems},
  JOURNAL = {Machine Learning Research},
  VOLUME = {1},
  PAGES = {143--160},
  YEAR = {2001},
  URL = {ftp://www.idiap.ch/pub/reports/2000/rr00-17.ps.gz},
  ANNOTE = {A paper relating to SVMTorch -classifier.}
}

@BOOK{cristianini00,
  AUTHOR = {Nello Cristianini and John Shawe-Taylor},
  TITLE = {An Introduction to Support Vector Machines and other kernel-base learning methods},
  PUBLISHER = {Cambridge University Press},
  YEAR = {2000},
  URL = {},
  ANNOTE = {An excellent introduction to support vectors though it requires knowledge of linear programming, Lagrangian formulation, linear algebra and so on. There is an appendix briefly presenting the math involved in the book, but it is far from meeting the needs of a layman. The book starts with Rosenblatt's perceptron, then introduces the use of kernels. The pac-learning related generalization is also portrayed as well as optimization theory. All this before the SVM issue is really opened.}
}

@MASTERSTHESIS{chin99support,
  AUTHOR = {K. K. Chin},
  TITLE = {Support Vector Machines applied to Speech Pattern Classification},
  SCHOOL = {Engineering Department, Cambridge University},
  YEAR = {1999},
  URL = {http://svr-www.eng.cam.ac.uk/~kkc21/thesis_main.ps.gz},
  ANNOTE = {A decent MSc thesis that deals with pretty much the basics of the SVM. The chapter on Tuning the RBF-kernel was very helpful}
}

@INPROCEEDINGS{cooley99classification,
  AUTHOR = {Robert Cooley},
  TITLE = {Classification of news stories using support vector machines},
  BOOKTITLE = {Proc. 16th International Joint Conference on Artificial Intelligence Text Mining Workshop},
  YEAR = {1999},
  URL = {citeseer.nj.nec.com/cooley99classification.html},
  ANNOTE = {}
}

@ARTICLE{drucker99support,
  AUTHOR = {Harris Drucker and Donghui Wu and Vladimir Vapnik},
  TITLE = {Support Vector Machines for {S}pam Categorization},
  JOURNAL = {IEEE-NN},
  VOLUME = {10},
  NUMBER = {5},
  PAGES = {1048--1054},
  YEAR = {1999}
}

@INPROCEEDINGS{dumais00,
  AUTHOR = {Susan Dumais and Hao Chen},
  TITLE = {Hierarchical Classification of Web Content},
  BOOKTITLE = {Proc. ACM SIGIR},
  YEAR = {2000},
  PAGES = {256 -- 263},
  URL = {http://research.microsoft.com/~sdumais/}
}

@ARTICLE{hearst98trends,
  AUTHOR = {Marti A. Hearst},
  TITLE = {Trends  Controversies: Support Vector Machines},
  JOURNAL = {IEEE Intelligent System},
  VOLUME = {13},
  NUMBER = {4},
  PAGES = {18-28},
  YEAR = {1998},
  ANNOTE = {}
}

@INPROCEEDINGS{joachims98text,
  AUTHOR = {Thorsten Joachims},
  TITLE = {Text categorization with support vector machines: learning with many relevant features},
  BOOKTITLE = {Proc. 10th European Conference on Machine Learning {ECML}-98},
  PAGES = {137--142},
  YEAR = {1998},
  URL = {http://www-ai.cs.uni-dortmund.de/DOKUMENTE/joachims_98a.ps.gz},
  ANNOTE = {}
}

@INCOLLECTION{joachims98making,
  AUTHOR = {Thorsten Joachims},
  TITLE = {Making large-scale SVM learning practical},
  BOOKTITLE = {Advances in Kernel Methods -- Support Vector Learning},
  PUBLISHER = {MIT Press},
  YEAR = {1998},
  PAGES = {169--185},
  EDITOR = {Bernhard Sch\"{o}lkopf and Chris Burges and Alex Smola},
  URL = {},
  ANNOTE = {}
}

@INPROCEEDINGS{joachims99transductive,
  AUTHOR = {Thorsten Joachims},
  TITLE = {Transductive inference for text classification using support vector machines},
  BOOKTITLE = {Proc. 16th International Conference on Machine Learning},
  PUBLISHER = {Morgan Kaufmann, San Francisco, CA},
  PAGES = {200--209},
  YEAR = {1999},
  URL = {},
  ANNOTE = {}
}

@INPROCEEDINGS{joachims00estimating,
  AUTHOR = {Thorsten Joachims},
  TITLE = {Estimating the Generalization Performance of an {SVM} Efficiently},
  BOOKTITLE = {Proc. 17th International Conf. on Machine Learning},
  PUBLISHER = {Morgan Kaufmann, San Francisco, CA},
  PAGES = {431--438},
  YEAR = {2000},
  URL = {},
  ANNOTE = {}
}

@INPROCEEDINGS{joachims99transducive,
  AUTHOR = {Thorsten Joachims},
  TITLE = {Transducive Inference for Text Classification Using Support Vector Machines},
  BOOKTITLE = {Proc. International Conference on Machine Learning ICML'99},
  YEAR = {1999},
  URL = {},
  ANNOTE = {}
}

@INCOLLECTION{scholkopf98,
  AUTHOR = {Bernhard Sch\"{o}lkopf and Chris Burges and Alex Smola},
  TITLE = {Introduction to Support Vector Learning},
  BOOKTITLE = {Advances in Kernel Methods -- Support Vector Learning},
  PUBLISHER = {MIT Press},
  YEAR = {1998},
  PAGES = {1--22},
  EDITOR = {Bernhard Sch\"{o}lkopf and Chris Burges and Alex Smola},
  URL = {},
  ANNOTE = {}
}

@PHDTHESIS{scholkopf97,
  AUTHOR = {Bernhard Sch\"{o}lkopf},
  TITLE = {Support Vector Learning},
  SCHOOL = {Technischen Universit\"{a}t Berlin},
  YEAR = {1997},
  NOTE = {Published by: R. Oldenbourg Verlag, Munich},
  URL = {},
  ANNOTE = {}
}