@inproceedings{1a6bb157c6a741e99f37828b4e57818d,
title = "A Probabilistic Semi-Supervised Approach with Triplet Markov Chains",
abstract = "Triplet Markov chains are general generative models for sequential data which take into account three kinds of random variables: (noisy) observations, their associated discrete labels and latent variables which aim at strengthening the distribution of the observations and their associated labels. However, in practice, we do not have at our disposal all the labels associated to the observations to estimate the parameters of such models. In this paper, we propose a general framework based on a variational Bayesian inference to train parameterized triplet Markov chain models in a semi-supervised context. The generality of our approach enables us to derive semisupervised algorithms for a variety of generative models for sequential Bayesian classification.",
keywords = "Generative Models, Semi-Supervised Learning, Triplet Markov Chains, Variational Inference",
author = "Katherine Morales and Yohan Petetin",
note = "Publisher Copyright: {\textcopyright} 2023 IEEE.; 33rd IEEE International Workshop on Machine Learning for Signal Processing, MLSP 2023 ; Conference date: 17-09-2023 Through 20-09-2023",
year = "2023",
month = jan,
day = "1",
doi = "10.1109/MLSP55844.2023.10285889",
language = "English",
series = "IEEE International Workshop on Machine Learning for Signal Processing, MLSP",
publisher = "IEEE Computer Society",
editor = "Danilo Comminiello and Michele Scarpiniti",
booktitle = "Proceedings of the 2023 IEEE 33rd International Workshop on Machine Learning for Signal Processing, MLSP 2023",
}