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Extracting support data for a given task

1995

Conference Paper

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We report a novel possibility for extracting a small subset of a data base which contains all the information necessary to solve a given classification task: using the Support Vector Algorithm to train three different types of handwritten digit classifiers, we observed that these types of classifiers construct their decision surface from strongly overlapping small (k: 4%) subsets of the data base. This finding opens up the possibiiity of compressing data bases significantly by disposing of the data which is not important for the solution of a given task. In addition, we show that the theory allows us to predict the classifier that will have the best generalization ability, based solely on performance on the training set and characteristics of the learning machines. This finding is important for cases where the amount of available data is limited.

Author(s): Schölkopf, B. and Burges, C. and Vapnik, V.
Book Title: First International Conference on Knowledge Discovery & Data Mining (KDD-95)
Journal: First International Conference on Knowledge Discovery & Data Mining
Pages: 252-257
Year: 1995
Month: August
Day: 0
Editors: UM Fayyad and R Uthurusamy
Publisher: AAAI Press

Department(s): Empirical Inference
Bibtex Type: Conference Paper (inproceedings)

Event Place: Montréal, Canada

Address: Menlo Park, CA, USA
Digital: 0
ISBN: 0-929280-82-2
Organization: Max-Planck-Gesellschaft
School: Biologische Kybernetik

Links: PDF

BibTex

@inproceedings{795,
  title = {Extracting support data for a given task},
  author = {Sch{\"o}lkopf, B. and Burges, C. and Vapnik, V.},
  journal = {First International Conference on Knowledge Discovery & Data Mining},
  booktitle = {First International Conference on Knowledge Discovery & Data Mining (KDD-95)},
  pages = {252-257},
  editors = {UM Fayyad and R Uthurusamy},
  publisher = {AAAI Press},
  organization = {Max-Planck-Gesellschaft},
  school = {Biologische Kybernetik},
  address = {Menlo Park, CA, USA},
  month = aug,
  year = {1995},
  month_numeric = {8}
}