6 citations · 12 across the 8 of their papers we have counts for
8 papers
Linear chain conditional random fields, hidden Markov models, and related classifiers
Elie Azeraf, Emmanuel Monfrini, Wojciech Pieczynski
Practitioners use Hidden Markov Models (HMMs) in different problems for about sixty years. Besides, Conditional Random Fields (CRFs) are an alternative to HMMs and appear in the li…
Deriving discriminative classifiers from generative models
Elie Azeraf, Emmanuel Monfrini, Wojciech Pieczynski
We deal with Bayesian generative and discriminative classifiers. Given a model distribution , with the observation and the target , one computes generative classifi…
On equivalence between linear-chain conditional random fields and hidden Markov chains
Elie Azeraf, Emmanuel Monfrini, Wojciech Pieczynski
Practitioners successfully use hidden Markov chains (HMCs) in different problems for about sixty years. HMCs belong to the family of generative models and they are often compared t…
Improving usual Naive Bayes classifier performances with Neural Naive Bayes based models
Elie Azeraf, Emmanuel Monfrini, Wojciech Pieczynski
Naive Bayes is a popular probabilistic model appreciated for its simplicity and interpretability. However, the usual form of the related classifier suffers from two major problems.…
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…
Highly Fast Text Segmentation With Pairwise Markov Chains
Elie Azeraf, Emmanuel Monfrini, Emmanuel Vignon +1
Natural Language Processing (NLP) models' current trend consists of using increasingly more extra-data to build the best models as possible. It implies more expensive computational…