A Discriminative Nonparametric Bayesian Model: Infinite Hidden Conditional Random Fields

Abstract

Nonparametric methods have been successfully applied to many existing graphical models with latent variables [3, 2, 7, 4]. In contrast to all previous work, the infinite Hidden Conditional Random Fields (iHCRF), introduced in this work, is the first, to our knowledge, discriminative bayesian nonparametric model.

Open PDF

Document Details

Document Type
Technical Report
Publication Date
Jan 01, 2013
Accession Number
AD1170736

Entities

People

  • Konstantinos Bousmalis
  • Louis-Philippe Morency
  • Maja Pantic
  • Stefanos Zafeiriou

Organizations

  • University of Southern California

Tags

Communities of Interest

  • Materials and Manufacturing Processes

DTIC Thesaurus Topics

  • Agreements
  • Bayesian Networks
  • Computer Vision
  • Hidden Markov Models
  • Image Segmentation
  • Information Processing
  • Information Systems
  • Markov Models
  • Models
  • Observation
  • Probability
  • Recognition
  • Sampling
  • Sequences
  • Transitions
  • Universities

Fields of Study

  • Computer science

Readers

  • Neuroscience
  • Statistical inference.

Technology Areas

  • AI & ML
  • AI & ML - Bayesian Inference
  • AI & ML - Machine Learning Algorithms