6 citations · 12 across the 10 of their papers we have counts for
4 papers · 1 filter
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…