0


概率确定性无限自动机

Probabilistic Deterministic Infinite Automata
课程网址: http://videolectures.net/nips2010_pfau_pdi/  
主讲教师: David Pfau
开课单位: 哥伦比亚大学
开课时间: 2011-03-25
课程语种: 英语
中文简介:
提出了一种新的概率确定性有限自动机贝叶斯非参数学习方法。我们定义并开发了一个具有无限多状态的PDFA,我们称之为概率确定性无限自动机(PDIA)。该模型中的后验预测推理,给定一个有限的训练序列,可以解释为在多个不同结构的PDFA上的平均,其中每个PDFA偏向于具有很少的状态。我们提出了一种预测分布平滑的新方法。我们在PDFA结构学习以及自然语言和DNA数据预测任务上测试了PDIA推断。结果表明,在隐马尔可夫模型的计算成本和层次平滑马尔可夫模型的存储需求之间,PDIA是一个有吸引力的折衷方案。
课程简介: We propose a novel Bayesian nonparametric approach to learning with probabilistic deterministic finite automata (PDFA). We define and develop and sampler for a PDFA with an infinite number of states which we call the probabilistic deterministic infinite automata (PDIA). Posterior predictive inference in this model, given a finite training sequence, can be interpreted as averaging over multiple PDFAs of varying structure, where each PDFA is biased towards having few states. We suggest that our method for averaging over PDFAs is a novel approach to predictive distribution smoothing. We test PDIA inference both on PDFA structure learning and on both natural language and DNA data prediction tasks. The results suggest that the PDIA presents an attractive compromise between the computational cost of hidden Markov models and the storage requirements of hierarchically smoothed Markov models.
关 键 词: 计算机科学; 机器学习; 贝叶斯学习
课程来源: 视频讲座网
最后编审: 2020-06-02:毛岱琦(课程编辑志愿者)
阅读次数: 145