The echo state conditional random field model for sequential data modeling
- 作者：Sotirios P. Chatzis ; soteri0s@mac.com ; Yiannis Demiris
- 关键词：Echo-state networks ; Conditional random fields ; Sequence segmentation ; Signal labeling
- 刊名：Expert Systems with Applications
- 出版年：2012
- 期刊代码：62_09574174
- 类别：et
- 出版时间：1 September, 2012
- 卷：39
- 期：11
- 页码：10303-10309
- 文件大小：606 K
摘要
Sequential data labeling is a fundamental task in machine learning applications, with speech and natural language processing, activity recognition in video sequences, and biomedical data analysis being characteristic such examples, to name just a few. The conditional random field (CRF), a log-linear model representing the conditional distribution of the observation labels, is one of the most successful approaches for sequential data labeling and classification, and has lately received significant attention in machine learning, as it achieves superb prediction performance in a variety of scenarios. Nevertheless, existing CRF formulations do not account for temporal dependencies between the observed variables - they only postulate Markovian interdependencies between the predicted label variables. To resolve these issues, in this paper we propose a non-linear hierarchical CRF formulation that combines the power of echo state networks to extract high level temporal features with the graphical framework of CRF models, yielding a powerful and scalable probabilistic model that we apply to signal labeling tasks.