6 citations · 8 across the 3 of their papers we have counts for
3 papers
Introducing the Hidden Neural Markov Chain framework
Elie Azeraf, Emmanuel Monfrini, Emmanuel Vignon +1
Nowadays, neural network models achieve state-of-the-art results in many areas as computer vision or speech processing. For sequential data, especially for Natural Language Process…
Using the Naive Bayes as a discriminative classifier
Elie Azeraf, Emmanuel Monfrini, Wojciech Pieczynski
For classification tasks, probabilistic models can be categorized into two disjoint classes: generative or discriminative. It depends on the posterior probability computation of th…
Hidden Markov Chains, Entropic Forward-Backward, and Part-Of-Speech Tagging
Elie Azeraf, Emmanuel Monfrini, Emmanuel Vignon +1
The ability to take into account the characteristics - also called features - of observations is essential in Natural Language Processing (NLP) problems. Hidden Markov Chain (HMC)…