标题： 分层半马尔可夫条件随机场用于递归序列数据 显示英文标题

标题： Hierarchical Semi-Markov Conditional Random Fields for Recursive Sequential Data

摘要： 受分层隐马尔可夫模型（HHMM）的启发，我们提出了分层半马尔可夫条件随机场（HSCRF），这是嵌入式无向马尔可夫链的推广，用于建模复杂的分层、嵌套马尔可夫过程。 它在判别框架中进行参数化，并具有多项式时间的学习和推理算法。 重要的是，我们考虑了部分监督学习，并提出了广义的部分监督学习和约束推理算法。 我们在两个应用中展示了HSCRF：（i）从室内监控摄像头识别日常生活活动（ADLs），以及（ii）名词短语切分。 我们证明了HSCRF能够在完全观测和部分观测数据情况下，以合理的准确性学习丰富的分层模型。 显示英文摘要

摘要： Inspired by the hierarchical hidden Markov models (HHMM), we present the hierarchical semi-Markov conditional random field (HSCRF), a generalisation of embedded undirectedMarkov chains tomodel complex hierarchical, nestedMarkov processes. It is parameterised in a discriminative framework and has polynomial time algorithms for learning and inference. Importantly, we consider partiallysupervised learning and propose algorithms for generalised partially-supervised learning and constrained inference. We demonstrate the HSCRF in two applications: (i) recognising human activities of daily living (ADLs) from indoor surveillance cameras, and (ii) noun-phrase chunking. We show that the HSCRF is capable of learning rich hierarchical models with reasonable accuracy in both fully and partially observed data cases.