A Discriminative Model Corresponding to Hierarchical HMMs
A Discriminative Model Corresponding to Hierarchical HMMs
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DOI：
10.1007/978-3-540-77226-2_39
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发表时间：
2007-12
影响因子：
4.6
通讯作者：
Takaaki Sugiura;Naoto Gotou;A. Hayashi
中科院分区：
文献类型：
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作者：
Takaaki Sugiura;Naoto Gotou;A. Hayashi
隐马尔可夫模型 (HMM) 是非常流行的序列数据生成模型。然而，最近的研究表明，在许多任务中，条件随机场（CRF）（一种判别模型）的表现比 HMM 更好。我们提出了分层隐藏条件随机场（HHCRF），这是一种与分层隐马尔可夫模型（HHMM）相对应的判别模型。 HHCRF 对给定观测值的上层状态的条件概率进行建模。较低级别的状态在模型定义中被隐藏和边缘化。我们为该模型开发了两种算法：参数学习算法，仅需要训练数据中上层的状态；边缘化维特比算法，通过边缘化下层的状态来计算上层最可能的状态序列。在一项涉及脑机接口分割脑电图 (EEG) 数据的实验中，HHCRF 优于 HHMM。
Hidden Markov Models (HMMs) are very popular generative models for sequence data. Recent work has, however, shown that on many tasks, Conditional Random Fields (CRFs), a type of discriminative model, perform better than HMMs. We propose Hierarchical Hidden Conditional Random Fields (HHCRFs), a discriminative model corresponding to hierarchical HMMs (HHMMs). HHCRFs model the conditional probability of the states at the upper levels given observations. The states at the lower levels are hidden and marginalized in the model definition. We have developed two algorithms for the model: a parameter learning algorithm that needs only the states at the upper levels in the training data and the marginalized Viterbi algorithm, which computes the most likely state sequences at the upper levels by marginalizing the states at the lower levels. In an experiment that involves segmenting electroencephalographic (EEG) data for a Brain-Computer Interface, HHCRFs outperform HHMMs.