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URN: urn:nbn:de:0030-drops-4146
URL: http://drops.dagstuhl.de/opus/volltexte/2006/414/
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Scheffer, Tobias

Multi-View Learning and Link Farm Discovery

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Abstract

The first part of this abstract focuses on estimation of mixture models for problems in which multiple views of the instances are available. Examples of this setting include clustering web pages or research papers that have intrinsic (text) and extrinsic (references) attributes. Mixture model estimation is a key problem for both semi-supervised and unsupervised learning. An appropriate optimization criterion quantifies the likelihood and the consensus among models in the individual views; maximizing this consensus minimizes a bound on the risk of assigning an instance to an incorrect mixture component. An EM algorithm maximizes this criterion. The second part of this abstract focuses on the problem of identifying link spam. Search engine optimizers inflate the page rank of a target site by spinning an artificial web for the sole purpose of providing inbound links to the target. Discriminating natural from artificial web sites is a difficult multi-view problem.

BibTeX - Entry

@InProceedings{scheffer:DSP:2006:414,
  author =	{Tobias Scheffer},
  title =	{Multi-View Learning and Link Farm Discovery},
  booktitle =	{Probabilistic, Logical and Relational Learning - Towards a Synthesis},
  year =	{2006},
  editor =	{Luc De Raedt and Thomas Dietterich and Lise Getoor  and Stephen H. Muggleton},
  number =	{05051},
  series =	{Dagstuhl Seminar Proceedings},
  ISSN =	{1862-4405},
  publisher =	{Internationales Begegnungs- und Forschungszentrum f{\"u}r Informatik (IBFI), Schloss Dagstuhl, Germany},
  address =	{Dagstuhl, Germany},
  URL =		{http://drops.dagstuhl.de/opus/volltexte/2006/414},
  annote =	{Keywords: Multi-view learning}
}

Keywords: Multi-view learning
Seminar: 05051 - Probabilistic, Logical and Relational Learning - Towards a Synthesis
Issue Date: 2006
Date of publication: 19.01.2006


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