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Kernel Methods for Graphs: A Comprehensive Approach

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Knowledge-Based Intelligent Information and Engineering Systems (KES 2008)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 5178))

Abstract

The development of learning algorithms for structured data, i.e. data that cannot be represented by numerical vectors, is a relevant challenge in machine learning. Kernel Methods, which is a leading machine learning technology for vectorial data, recently tackled the structured data. In this paper we focus our attention on Kernel Methods that face up to data that can be represented by means of graphs, by providing an in-depth review through a comprehensive approach to the research hints and the main open problems in this area of research.

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Authors and Affiliations

  1. Department of Applied Science, University of Naples Parthenope, Centro Direzionale Isola C4, 80143, Naples, Italy

    Francesco Camastra & Alfredo Petrosino

Authors
  1. Francesco Camastra
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  2. Alfredo Petrosino
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Editor information

Ignac Lovrek Robert J. Howlett Lakhmi C. Jain

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© 2008 Springer-Verlag Berlin Heidelberg

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Camastra, F., Petrosino, A. (2008). Kernel Methods for Graphs: A Comprehensive Approach. In: Lovrek, I., Howlett, R.J., Jain, L.C. (eds) Knowledge-Based Intelligent Information and Engineering Systems. KES 2008. Lecture Notes in Computer Science(), vol 5178. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-85565-1_82

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  • DOI: https://doi.org/10.1007/978-3-540-85565-1_82

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-85564-4

  • Online ISBN: 978-3-540-85565-1

  • eBook Packages: Computer ScienceComputer Science (R0)

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