Learning Structured Classifiers with Dual Coordinate Ascent

Abstract

We present a unified framework for online learning of structured classifiers that handles a wide family of convex loss functions, properly including CRFs, structured SVMs, and the structured perceptron. We introduce a new aggressive online algorithm that optimizes any loss in this family. For the structured hinge loss, this algorithm reduces to 1-best MIRA; in general, it can be regarded as a dual coordinate ascent algorithm. The approximate inference scenario is also addressed. Our experiments on two NLP problems show that the algorithm converges to accurate models at least as fast as stochastic gradient descent, without the need to specify any learning rate parameter.

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Document Details

Document Type
Technical Report
Publication Date
Jun 01, 2010
Accession Number
ADA532568

Entities

People

  • AndrĂ© F. Martins
  • Eric P. Xing
  • Kevin Gimpel
  • Mario A. Figueiredo
  • Noah A. Smith
  • Pedro M. Aguiar

Organizations

  • Carnegie Mellon University

Tags

Communities of Interest

  • Autonomy

DTIC Thesaurus Topics

  • Algorithms
  • Artificial Intelligence Software
  • Computational Science
  • Computer Languages
  • Computer Science
  • Distance Learning
  • Homosexuality
  • Information Science
  • Learning
  • Linear Programming
  • Machine Learning
  • Markov Models
  • Named Entity Recognition
  • Optimization
  • Probabilistic Models
  • Probability
  • Supervised Machine Learning

Fields of Study

  • Computer science

Readers

  • Neural Network Machine Learning.
  • Operations Research

Technology Areas

  • AI & ML
  • AI & ML - Machine Learning Algorithms
  • AI & ML - Neural Networks