6 citations · 12 across the 8 of their papers we have counts for
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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.…
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)…