Graph-based transduction with confidence

  • Orbach M
  • Crammer K
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We present a new multi-class graph-based transduction algorithm. Examples are
associated with vertices in an undirected weighted graph and edge weights
correspond to a similarity measure between examples. Typical algorithms in such
a setting perform label propagation between neighbours, ignoring the quality, or
estimated quality, in the labeling of various nodes. We introduce an additional
quantity of confidence in label assignments, and learn them jointly with the
weights, while using them to dynamically tune the influence of each vertex on
its neighbours. We cast learning as a convex optimization problem, and derive an
efficient iterative algorithm for solving it. Empirical evaluations on seven NLP
data sets demonstrate our algorithm improves over other state-of-the-art graph-
based transduction algorithms. Page %P Close Plain text Look Inside
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gaussian fields and harmonic functions. In: ICML (2003) About this Chapter
Title Graph-Based Transduction with Confidence Book Title Machine Learning and
Knowledge Discovery in Databases Book Subtitle European Conference, ECML PKDD
2012, Bristol, UK, September 24-28, 2012. Proceedings, Part II Pages pp 323-338
Copyright 2012 DOI 10.1007/978-3-642-33486-3_21 Print ISBN 978-3-642-33485-6
Online ISBN 978-3-642-33486-3 Series Title Lecture Notes in Computer Science
Series Volume 7524 Series ISSN 0302-9743 Publisher Springer Berlin Heidelberg
Copyright Holder Springer-Verlag Berlin Heidelberg Additional Links About this
Book Topics Data Mining and Knowledge Discovery Artificial Intelligence (incl.
Robotics) Pattern Recognition Discrete Mathematics in Computer Science
Probability and Statistics in Computer Science Information Storage and Retrieval
Industry Sectors Electronics Telecommunications IT & Software eBook Packages
eBook Package english Computer Science eBook Package english full Collection

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  • Matan Orbach

  • Koby Crammer

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