图 书 馆
直链条件随机场渐近理论Asymptotic Theory for Linear-Chain Conditional Random Fields |
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| 课程网址: | http://videolectures.net/aistats2011_sinn_asymptotic/ |
| 主讲教师: | Mathieu Sinn |
| 开课单位: | 滑铁卢大学 |
| 开课时间: | 信息不详。欢迎您在右侧留言补充。 |
| 课程语种: | 英语 |
| 中文简介: |  |
| 课程简介: | In this theoretical paper we develop an asymptotic theory for Linear-Chain Conditional Random Fields (L-CRFs) and apply it to derive conditions under which the Maximum Likelihood Estimates (MLEs) of the model weights are strongly consistent. We first define L-CRFs for infinite sequences and analyze some of their basic properties. Then we establish conditions under which ergodicity of the observations implies ergodicity of the joint sequence of observations and labels. This result is the key ingredient to derive conditions for strong consistency of the MLEs. Interesting findings are that the consistency crucially depends on the limit behavior of the Hessian of the likelihood function and that, asymptotically, the state feature functions do not matter. |
| 关 键 词: | 机器学习; 最大似然估计; 函数 |
| 课程来源: | 视频讲座网 |
| 最后编审: | 2019-12-19：cwx |
| 阅读次数: | 34 |