Article

Learning from interpretations: a rooted kernel for ordered hypergraphs

Authors:

Gabriel Wachman,

Roni KhardonAuthors Info & Claims

ICML '07: Proceedings of the 24th international conference on Machine learning

Pages 943 - 950

https://doi.org/10.1145/1273496.1273615

Published: 20 June 2007 Publication History

Abstract

The paper presents a kernel for learning from ordered hypergraphs, a formalization that captures relational data as used in Inductive Logic Programming (ILP). The kernel generalizes previous approaches to graph kernels in calculating similarity based on walks in the hypergraph. Experiments on challenging chemical datasets demonstrate that the kernel outperforms existing ILP methods, and is competitive with state-of-the-art graph kernels. The experiments also demonstrate that the encoding of graph data can affect performance dramatically, a fact that can be useful beyond kernel methods.

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Cited By

Feng YHan JYing SGao Y(2024)Hypergraph Isomorphism ComputationIEEE Transactions on Pattern Analysis and Machine Intelligence10.1109/TPAMI.2024.3353199(1-17)Online publication date: 2024
https://doi.org/10.1109/TPAMI.2024.3353199
Böhmová KChalopin JMihalák MProietti GWidmayer P(2018)Sequence Hypergraphs: Paths, Flows, and CutsAdventures Between Lower Bounds and Higher Altitudes10.1007/978-3-319-98355-4_12(191-215)Online publication date: 9-Aug-2018
https://doi.org/10.1007/978-3-319-98355-4_12
Amiri KFarah MFarah I(2017)Fuzzy hypergraph of concepts for semantic annotation of remotely sensed images2017 International Conference on Advanced Technologies for Signal and Image Processing (ATSIP)10.1109/ATSIP.2017.8075516(1-8)Online publication date: May-2017
https://doi.org/10.1109/ATSIP.2017.8075516
Show More Cited By

Learning from interpretations: a rooted kernel for ordered hypergraphs
1. Mathematics of computing
  1. Discrete mathematics
    1. Graph theory

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cover image ACM Other conferences

ICML '07: Proceedings of the 24th international conference on Machine learning

June 2007

1233 pages

ISBN:9781595937933

DOI:10.1145/1273496

Editor:
Zoubin Ghahramani
University of Cambridge, United Kingdom

Copyright © 2007 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Publication History

Published: 20 June 2007

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ICML '07 & ILP '07

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ICML '07 & ILP '07: The 24th Annual International Conference on Machine Learning held in conjunction with the 2007 International Conference on Inductive Logic Programming

June 20 - 24, 2007

Oregon, Corvalis, USA

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Cited By

Feng YHan JYing SGao Y(2024)Hypergraph Isomorphism ComputationIEEE Transactions on Pattern Analysis and Machine Intelligence10.1109/TPAMI.2024.3353199(1-17)Online publication date: 2024
https://doi.org/10.1109/TPAMI.2024.3353199
Böhmová KChalopin JMihalák MProietti GWidmayer P(2018)Sequence Hypergraphs: Paths, Flows, and CutsAdventures Between Lower Bounds and Higher Altitudes10.1007/978-3-319-98355-4_12(191-215)Online publication date: 9-Aug-2018
https://doi.org/10.1007/978-3-319-98355-4_12
Amiri KFarah MFarah I(2017)Fuzzy hypergraph of concepts for semantic annotation of remotely sensed images2017 International Conference on Advanced Technologies for Signal and Image Processing (ATSIP)10.1109/ATSIP.2017.8075516(1-8)Online publication date: May-2017
https://doi.org/10.1109/ATSIP.2017.8075516
Dumană?Ić SBlockeel H(2017)An expressive dissimilarity measure for relational clustering using neighbourhood treesMachine Language10.1007/s10994-017-5644-6106:9-10(1523-1545)Online publication date: 1-Oct-2017
https://dl.acm.org/doi/10.1007/s10994-017-5644-6
Bai LEscolano FHancock E(2016)Depth-based hypergraph complexity traces from directed line graphsPattern Recognition10.1016/j.patcog.2016.01.00454:C(229-240)Online publication date: 1-Jun-2016
https://dl.acm.org/doi/10.1016/j.patcog.2016.01.004
Böhmová KChalopin JMihalák MProietti GWidmayer P(2016)Sequence HypergraphsRevised Selected Papers of the 42nd International Workshop on Graph-Theoretic Concepts in Computer Science - Volume 994110.1007/978-3-662-53536-3_24(282-294)Online publication date: 22-Jun-2016
https://dl.acm.org/doi/10.1007/978-3-662-53536-3_24
Bai LHancock ERen PEscolano F(2014)Directed Depth-Based Complexity Traces of Hypergraphs from Directed Line GraphsProceedings of the 2014 22nd International Conference on Pattern Recognition10.1109/ICPR.2014.807(3874-3879)Online publication date: 24-Aug-2014
https://dl.acm.org/doi/10.1109/ICPR.2014.807
Bai LRen PHancock E(2014)A Hypergraph Kernel from Isomorphism TestsProceedings of the 2014 22nd International Conference on Pattern Recognition10.1109/ICPR.2014.665(3880-3885)Online publication date: 24-Aug-2014
https://dl.acm.org/doi/10.1109/ICPR.2014.665
Li LLi TLeonardi SPanconesi AFerragina PGionis A(2013)News recommendation via hypergraph learningProceedings of the sixth ACM international conference on Web search and data mining10.1145/2433396.2433436(305-314)Online publication date: 4-Feb-2013
https://dl.acm.org/doi/10.1145/2433396.2433436
Bai LHancock ERen P(2012)A jensen-shannon kernel for hypergraphsProceedings of the 2012 Joint IAPR international conference on Structural, Syntactic, and Statistical Pattern Recognition10.1007/978-3-642-34166-3_20(181-189)Online publication date: 7-Nov-2012
https://dl.acm.org/doi/10.1007/978-3-642-34166-3_20
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