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Large Margin Methods for Structured and Interdependent Output Variables

2005

Article

ei


Learning general functional dependencies between arbitrary input and output spaces is one of the key challenges in computational intelligence. While recent progress in machine learning has mainly focused on designing flexible and powerful input representations, this paper addresses the complementary issue of designing classification algorithms that can deal with more complex outputs, such as trees, sequences, or sets. More generally, we consider problems involving multiple dependent output variables, structured output spaces, and classification problems with class attributes. In order to accomplish this, we propose to appropriately generalize the well-known notion of a separation margin and derive a corresponding maximum-margin formulation. While this leads to a quadratic program with a potentially prohibitive, i.e. exponential, number of constraints, we present a cutting plane algorithm that solves the optimization problem in polynomial time for a large class of problems. The proposed method has important applications in areas such as computational biology, natural language processing, information retrieval/extraction, and optical character recognition. Experiments from various domains involving different types of output spaces emphasize the breadth and generality of our approach.

Author(s): Tsochantaridis, I. and Joachims, T. and Hofmann, T. and Altun, Y.
Journal: Journal of Machine Learning Research
Volume: 6
Pages: 1453-1484
Year: 2005
Month: September
Day: 0

Department(s): Empirical Inference
Bibtex Type: Article (article)

Digital: 0
Language: en
Organization: Max-Planck-Gesellschaft
School: Biologische Kybernetik

Links: PDF

BibTex

@article{5701,
  title = {Large Margin Methods for Structured and Interdependent Output Variables},
  author = {Tsochantaridis, I. and Joachims, T. and Hofmann, T. and Altun, Y.},
  journal = {Journal of Machine Learning Research},
  volume = {6},
  pages = {1453-1484},
  organization = {Max-Planck-Gesellschaft},
  school = {Biologische Kybernetik},
  month = sep,
  year = {2005},
  month_numeric = {9}
}