Appropriate Kernel Functions for Support Vector Machine Learning with Sequences of Symbolic Data-Reference-Cited by-同舟云学术

Appropriate Kernel Functions for Support Vector Machine Learning with Sequences of Symbolic Data

Published:2005 Issue: Volume: Page:256-280
ISSN:0302-9743
Container-title:Lecture Notes in Computer Science
language:
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Author:

Vanschoenwinkel Bram,Manderick Bernard

Publisher

Springer Berlin Heidelberg

Link

http://link.springer.com/content/pdf/10.1007/11559887_16.pdf

Reference28 articles.

1. Schölkopf, B.: The kernel trick for distances. Technical report, Microsoft Research (2000)

2. Joachims, T.: Learning to Classify Text Using Support Vector Machines. Kluwer Academic Publishers, Dordrecht (2002)

3. Vanschoenwinkel, B., Manderick, B.: A weighted polynomial information gain kernel for resolving pp attachment ambiguities with support vector machines. In: The Proceedings of the Eighteenth International Joint Conferences on Artificial Intelligence (IJCAI 2003), pp.133–138 (2003)

4. Vapnik, V.: The Nature of Statistical Learning Theory. Springer, Heidelberg (1998)

5. Cristianini, N., Shawe-Taylor, J.: An Introduction to Support Vector Machines and other Kernel-based Learning Methods. Cambridge University Press, Cambridge (2000)

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