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 semi-supervised algorithms for a variety of generative models for sequential Bayesian classification.

翻译：三元马尔可夫链是一类通用的序列数据生成模型，其考虑了三种随机变量：（含噪声的）观测值、与其对应的离散标签以及旨在强化观测值及其标签分布的潜变量。然而在实际应用中，我们无法获取所有观测值对应的完整标签来估计此类模型的参数。本文提出一种基于变分贝叶斯推断的通用框架，用于在半监督场景下训练参数化的三元马尔可夫链模型。该方法的通用性使我们能够针对序列贝叶斯分类中的多种生成模型推导出半监督算法。