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Probabilistic Frequent Subtree Kernels.

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Probabilistic Frequent Subtree Kernels. / Welke, Pascal; Horváth, Tamás; Wrobel, Stefan.
Probabilistic Frequent Subtree Kernels.. Vol. 9067 Springer, Cham, 2016. p. 179-193 (Lecture Notes in Computer Science; Vol. 9607).

Research output: Contribution in Book/Report/Proceedings - With ISBN/ISSN › Conference contribution/Paper › peer-review

Harvard

Welke, P, Horváth, T & Wrobel, S 2016, Probabilistic Frequent Subtree Kernels. in Probabilistic Frequent Subtree Kernels.. vol. 9067, Lecture Notes in Computer Science, vol. 9607, Springer, Cham, pp. 179-193, New Frontiers in Mining Complex Patterns , Porto, 7/09/15. https://doi.org/10.1007/978-3-319-39315-5_12

APA

Welke, P., Horváth, T., & Wrobel, S. (2016). Probabilistic Frequent Subtree Kernels. In Probabilistic Frequent Subtree Kernels. (Vol. 9067, pp. 179-193). (Lecture Notes in Computer Science; Vol. 9607). Springer, Cham. https://doi.org/10.1007/978-3-319-39315-5_12

Vancouver

Welke P, Horváth T, Wrobel S. Probabilistic Frequent Subtree Kernels. In Probabilistic Frequent Subtree Kernels.. Vol. 9067. Springer, Cham. 2016. p. 179-193. (Lecture Notes in Computer Science). doi: 10.1007/978-3-319-39315-5_12

Author

Welke, Pascal ; Horváth, Tamás ; Wrobel, Stefan. / Probabilistic Frequent Subtree Kernels. Probabilistic Frequent Subtree Kernels.. Vol. 9067 Springer, Cham, 2016. pp. 179-193 (Lecture Notes in Computer Science).

Bibtex

@inproceedings{aa5c8e0a736d4d0a8b6b21765690139b,
title = "Probabilistic Frequent Subtree Kernels.",
abstract = "We propose a new probabilistic graph kernel. It is defined by the set of frequent subtrees generated from a small random sample of spanning trees of the transaction graphs. In contrast to the ordinary frequent subgraph kernel it can be computed efficiently for any arbitrary graphs. Due to its probabilistic nature, the embedding function corresponding to our graph kernel is not always correct. Our empirical results on artificial and real-world chemical datasets, however, demonstrate that the graph kernel we propose is much faster than other frequent pattern based graph kernels, with only marginal loss in predictive accuracy.",
author = "Pascal Welke and Tam{\'a}s Horv{\'a}th and Stefan Wrobel",
note = "DBLP's bibliographic metadata records provided through http://dblp.org/search/publ/api are distributed under a Creative Commons CC0 1.0 Universal Public Domain Dedication. Although the bibliographic metadata records are provided consistent with CC0 1.0 Dedication, the content described by the metadata records is not. Content may be subject to copyright, rights of privacy, rights of publicity and other restrictions.; New Frontiers in Mining Complex Patterns ; Conference date: 07-09-2015 Through 07-09-2015",
year = "2016",
month = may,
day = "18",
doi = "10.1007/978-3-319-39315-5_12",
language = "Undefined/Unknown",
isbn = "9783319393148",
volume = "9067",
series = "Lecture Notes in Computer Science",
publisher = "Springer, Cham",
pages = "179--193",
booktitle = "Probabilistic Frequent Subtree Kernels.",

}

RIS

TY - GEN

T1 - Probabilistic Frequent Subtree Kernels.

AU - Welke, Pascal

AU - Horváth, Tamás

AU - Wrobel, Stefan

N1 - DBLP's bibliographic metadata records provided through http://dblp.org/search/publ/api are distributed under a Creative Commons CC0 1.0 Universal Public Domain Dedication. Although the bibliographic metadata records are provided consistent with CC0 1.0 Dedication, the content described by the metadata records is not. Content may be subject to copyright, rights of privacy, rights of publicity and other restrictions.

PY - 2016/5/18

Y1 - 2016/5/18

N2 - We propose a new probabilistic graph kernel. It is defined by the set of frequent subtrees generated from a small random sample of spanning trees of the transaction graphs. In contrast to the ordinary frequent subgraph kernel it can be computed efficiently for any arbitrary graphs. Due to its probabilistic nature, the embedding function corresponding to our graph kernel is not always correct. Our empirical results on artificial and real-world chemical datasets, however, demonstrate that the graph kernel we propose is much faster than other frequent pattern based graph kernels, with only marginal loss in predictive accuracy.

AB - We propose a new probabilistic graph kernel. It is defined by the set of frequent subtrees generated from a small random sample of spanning trees of the transaction graphs. In contrast to the ordinary frequent subgraph kernel it can be computed efficiently for any arbitrary graphs. Due to its probabilistic nature, the embedding function corresponding to our graph kernel is not always correct. Our empirical results on artificial and real-world chemical datasets, however, demonstrate that the graph kernel we propose is much faster than other frequent pattern based graph kernels, with only marginal loss in predictive accuracy.

U2 - 10.1007/978-3-319-39315-5_12

DO - 10.1007/978-3-319-39315-5_12

M3 - Conference contribution/Paper

SN - 9783319393148

VL - 9067

T3 - Lecture Notes in Computer Science

SP - 179

EP - 193

BT - Probabilistic Frequent Subtree Kernels.

PB - Springer, Cham

T2 - New Frontiers in Mining Complex Patterns

Y2 - 7 September 2015 through 7 September 2015

ER -