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Exploiting Cluster-Structure to Predict the Labeling of a Graph

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Algorithmic Learning Theory (ALT 2008)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 5254))

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Abstract

The nearest neighbor and the perceptron algorithms are intuitively motivated by the aims to exploit the “cluster” and “linear separation” structure of the data to be classified, respectively. We develop a new online perceptron-like algorithm, Pounce, to exploit both types of structure. We refine the usual margin-based analysis of a perceptron-like algorithm to now additionally reflect the cluster-structure of the input space. We apply our methods to study the problem of predicting the labeling of a graph. We find that when both the quantity and extent of the clusters are small we may improve arbitrarily over a purely margin-based analysis.

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Author information

Authors and Affiliations

  1. Department of Computer Science, University College London, Gower Street, London, WC1E 6BT, England, UK

    Mark Herbster

Authors
  1. Mark Herbster
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Editor information

Editors and Affiliations

  1. Computer Science and Engineering, University of California, San Diego, USA

    Yoav Freund

  2. Department of Computer Science and Information Theory, Department of Computer Science and Budapest University of Technology and Economics, Stoczek u. 2, 1521, Budapest, Hungary

    László Györfi

  3. Department of Math., Stat. and Comp. Sci,, University of Illinois, 851 S. Morgan, IL 60607-7045, Chicago, USA

    György Turán

  4. Division of Computer Science, Hokkaido University, N-14, W-9, 060-0814, Sapporo, Japan

    Thomas Zeugmann

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© 2008 Springer-Verlag Berlin Heidelberg

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Herbster, M. (2008). Exploiting Cluster-Structure to Predict the Labeling of a Graph. In: Freund, Y., Györfi, L., Turán, G., Zeugmann, T. (eds) Algorithmic Learning Theory. ALT 2008. Lecture Notes in Computer Science(), vol 5254. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-87987-9_9

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  • DOI: https://doi.org/10.1007/978-3-540-87987-9_9

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-87986-2

  • Online ISBN: 978-3-540-87987-9

  • eBook Packages: Computer ScienceComputer Science (R0)

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