Linear prediction models with graph regularization for Web-page categorization

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

We present a risk minimization formulation for learning from both text and graph structures which is motivated by the problem of collective inference for hypertext document categorization. The method is based on graph regularization formulated as a well-formed convex optimization problem. We present numerical algorithms for our formulation, and show that such combination of local text features and link information can lead to improved predictive accuracy.

Original languageEnglish (US)
Title of host publicationKDD 2006
Subtitle of host publicationProceedings of the Twelfth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining
PublisherAssociation for Computing Machinery
Pages821-826
Number of pages6
ISBN (Print)1595933395, 9781595933393
DOIs
StatePublished - 2006
Externally publishedYes
EventKDD 2006: 12th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining - Philadelphia, PA, United States
Duration: Aug 20 2006Aug 23 2006

Publication series

NameProceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining
Volume2006

Other

OtherKDD 2006: 12th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining
Country/TerritoryUnited States
CityPhiladelphia, PA
Period8/20/068/23/06

Keywords

  • Collective inference
  • Document classification
  • Graph and relational learning
  • Regularization
  • Semi-supervised learning

ASJC Scopus subject areas

  • Software
  • Information Systems

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