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  Extracting support data for a given task

Schölkopf, B., Burges, C., & Vapnik, V. (1995). Extracting support data for a given task. In U. Fayyad, & R. Uthurusamy (Eds.), First International Conference on Knowledge Discovery & Data Mining (KDD-95) (pp. 252-257). Menlo Park, CA, USA: AAAI Press.

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Item Permalink: http://hdl.handle.net/11858/00-001M-0000-0013-EC66-3 Version Permalink: http://hdl.handle.net/21.11116/0000-0005-F597-C
Genre: Conference Paper

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 Creators:
Schölkopf, B1, Author              
Burges, C, Author
Vapnik, V, Author              
Affiliations:
1External Organizations, ou_persistent22              

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 Abstract: 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.

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 Dates: 1995-08
 Publication Status: Published in print
 Pages: -
 Publishing info: -
 Table of Contents: -
 Rev. Type: -
 Identifiers: BibTex Citekey: 795
 Degree: -

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Title: First International Conference on Knowledge Discovery & Data Mining (KDD 1995)
Place of Event: Montréal, Canada
Start-/End Date: 1995-08-20 - 1995-08-21

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Title: First International Conference on Knowledge Discovery & Data Mining (KDD-95)
Source Genre: Proceedings
 Creator(s):
Fayyad, UM, Editor
Uthurusamy, R, Editor
Affiliations:
-
Publ. Info: Menlo Park, CA, USA : AAAI Press
Pages: 345 Volume / Issue: - Sequence Number: - Start / End Page: 252 - 257 Identifier: ISBN: 0-929280-82-2