6 citations · 12 across the 10 of their papers we have counts for
7 papers · 1 filter
Pairwise Markov Chains for Volatility Forecasting
Elie Azeraf
The Pairwise Markov Chain (PMC) is a probabilistic graphical model extending the well-known Hidden Markov Model. This model, although highly effective for many tasks, has been scar…
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…