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Found 8 papers, 0 papers with code
Linear chain conditional random fields, hidden Markov models, and related classifiers
Practitioners use Hidden Markov Models (HMMs) in different problems for about sixty years.
Deriving discriminative classifiers from generative models
A discriminative model is directly given by $p(x | y)$, which is used to compute discriminative classifiers.
On equivalence between linear-chain conditional random fields and hidden Markov chains
HMCs belong to the family of generative models and they are often compared to discriminative models, like conditional random fields (CRFs).
Improving usual Naive Bayes classifier performances with Neural Naive Bayes based models
Naive Bayes is a popular probabilistic model appreciated for its simplicity and interpretability.
Introducing the Hidden Neural Markov Chain framework
However, if many works create extensions and improvements of the RNN, few have focused on developing other ways for sequential data processing with neural networks in a "term-to-term" way.
Highly Fast Text Segmentation With Pairwise Markov Chains
Natural Language Processing (NLP) models' current trend consists of using increasingly more extra-data to build the best models as possible.
Using the Naive Bayes as a discriminative classifier
Related to this point, we show that the Logistic Regression can be viewed as a particular case of the Naive Bayes used in a discriminative way.
Hidden Markov Chains, Entropic Forward-Backward, and Part-Of-Speech Tagging
We illustrate the efficiency of HMC using EFB in Part-Of-Speech Tagging, showing its superiority over MEMM based restoration.
