Polynomial Conditional Random Fields for Signal Processing

We describe Polynomial Conditional Random Fields for signal processing tasks. It is a hybrid model that combines the ability of Polynomial Hidden Markov models for modeling complex dynamic signals and the discriminant power of Conditional Random Fields. We detail the learning of these models and report experimental results on handwriting recognition.1

• Type: Conference
• Year: 2006
• Where: ECAI
• Authors: Trinh Minh Tri Do, Thierry Artières
• Keywords: Artificial Intelligence, Conditional Random Fields, ECAI 2006, Polynomial Hidden Markov, Signal Processing Tasks