Margin-based active learning for structured output spaces

Dan Roth, Kevin Small

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


In many complex machine learning applications there is a need to learn multiple interdependent output variables, where knowledge of these interdependencies can be exploited to improve the global performance. Typically, these structured output scenarios are also characterized by a high cost associated with obtaining supervised training data, motivating the study of active learning for these situations. Starting with active learning approaches for multiclass classification, we first design querying functions for selecting entire structured instances, exploring the tradeoff between selecting instances based on a global margin or a combination of the margin of local classifiers. We then look at the setting where subcomponents of the structured instance can be queried independently and examine the benefit of incorporating structural information in such scenarios. Empirical results on both synthetic data and the semantic role labeling task demonstrate a significant reduction in the need for supervised training data when using the proposed methods.

Original languageEnglish (US)
Title of host publicationMachine Learning
Subtitle of host publicationECML 2006 - 17th European Conference on Machine Learning, Proceedings
Number of pages12
ISBN (Print)354045375X, 9783540453758
StatePublished - 2006
Event17th European Conference on Machine Learning, ECML 2006 - Berlin, Germany
Duration: Sep 18 2006Sep 22 2006

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume4212 LNAI
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Other17th European Conference on Machine Learning, ECML 2006

ASJC Scopus subject areas

  • Theoretical Computer Science
  • General Computer Science


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