Fast on-line kernel learning for trees

Fabio Aiolli, Giovanni Da San Martino, Alessandro Sperduti, Alessandro Moschitti

Research output: Chapter in Book/Report/Conference proceedingConference contribution

10 Citations (Scopus)

Abstract

Kernel methods have been shown to be very effective for applications requiring the modeling of structured objects. However kernels for structures usually are too computational demanding to be applied to complex learning algorithms, e.g. Support Vector Machines. Consequently, in order to apply kernels to large amount of structured data, we need fast on-line algorithms along with an efficiency optimization of kernel-based computations. In this paper, we optimize this computation by representing set of trees by minimal Direct Acyclic Graphs (DAGs) allowing us i) to reduce the storage requirements and U) to speed up the evaluation on large number of trees as it can be done 'one-shot' by computing kernels over DAGs. The experiments on predicate argument subtrees from PropBank data show that substantial computational savings can be obtained for the perceptron algorithm.

Original languageEnglish
Title of host publicationProceedings - Sixth International Conference on Data Mining, ICDM 2006
Pages787-791
Number of pages5
DOIs
Publication statusPublished - 1 Dec 2006
Event6th International Conference on Data Mining, ICDM 2006 - Hong Kong, China
Duration: 18 Dec 200622 Dec 2006

Publication series

NameProceedings - IEEE International Conference on Data Mining, ICDM
ISSN (Print)1550-4786

Other

Other6th International Conference on Data Mining, ICDM 2006
CountryChina
CityHong Kong
Period18/12/0622/12/06

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ASJC Scopus subject areas

  • Engineering(all)

Cite this

Aiolli, F., Da San Martino, G., Sperduti, A., & Moschitti, A. (2006). Fast on-line kernel learning for trees. In Proceedings - Sixth International Conference on Data Mining, ICDM 2006 (pp. 787-791). [4053103] (Proceedings - IEEE International Conference on Data Mining, ICDM). https://doi.org/10.1109/ICDM.2006.69