图 书 馆
一种用于状态持久性系统的HDP-HMMAn HDP-HMM for Systems with State Persistence |
|
| 课程网址: | http://videolectures.net/icml08_fox_ahh/ |
| 主讲教师: | Emily Fox |
| 开课单位: | 华盛顿大学 |
| 开课时间: | 2009-08-29 |
| 课程语种: | 英语 |
| 中文简介: |  |
| 课程简介: | The hierarchical Dirichlet process hidden Markov model (HDP-HMM) is a flexible, nonparametric model which allows state spaces of unknown size to be learned from data. We demonstrate some limitations of the original HDP-HMM formulation, and propose a sticky extension which allows more robust learning of smoothly varying dynamics. Using DP mixtures, this formulation also allows learning of more complex, multimodal emission distributions. We further develop a sampling algorithm that employs a truncated approximation of the DP to jointly resample the full state sequence, greatly improving mixing rates. Via extensive experiments with synthetic data and the NIST speaker diarization database, we demonstrate the advantages of our sticky extension, and the utility of the HDP-HMM in real-world applications. |
| 关 键 词: | 隐马尔可夫模型; 非参数模型; 粘性扩展 |
| 课程来源: | 视频讲座网 |
| 最后编审: | 2019-04-18：cwx |
| 阅读次数: | 98 |