English
 
User Manual Privacy Policy Disclaimer Contact us
  Advanced SearchBrowse

Item

ITEM ACTIONSEXPORT
  Large Margin Methods for Structured and Interdependent Output Variables

Tsochantaridis, I., Joachims, T., Hofmann, T., & Altun, Y. (2005). Large Margin Methods for Structured and Interdependent Output Variables. The Journal of Machine Learning Research, 6, 1453-1484.

Item is

Basic

show hide
Item Permalink: http://hdl.handle.net/11858/00-001M-0000-0013-D423-7 Version Permalink: http://hdl.handle.net/21.11116/0000-0004-D786-2
Genre: Journal Article

Files

show Files

Creators

show
hide
 Creators:
Tsochantaridis, I, Author
Joachims, T, Author
Hofmann, T1, Author              
Altun, Y1, Author              
Affiliations:
1External Organizations, ou_persistent22              

Content

show
hide
Free keywords: -
 Abstract: 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.

Details

show
hide
Language(s):
 Dates: 2005-09
 Publication Status: Published in print
 Pages: -
 Publishing info: -
 Table of Contents: -
 Rev. Type: -
 Identifiers: BibTex Citekey: 5701
 Degree: -

Event

show

Legal Case

show

Project information

show

Source 1

show
hide
Title: The Journal of Machine Learning Research
Source Genre: Journal
 Creator(s):
Affiliations:
Publ. Info: Cambridge, MA : MIT Press
Pages: - Volume / Issue: 6 Sequence Number: - Start / End Page: 1453 - 1484 Identifier: ISSN: 1532-4435
CoNE: https://pure.mpg.de/cone/journals/resource/111002212682020_1