图 书 馆
语法推理作为主成分分析问题Grammatical Inference as a Principal Component Analysis Problem |
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| 课程网址: | https://videolectures.net/videos/icml09_bailly_gipcap |
| 主讲教师: | Raphaël Bailly |
| 开课单位: | 会议 |
| 开课时间: | 2009-08-26 |
| 课程语种: | 英语 |
| 中文简介: |  |
| 课程简介: | One of the main problems in probabilistic grammatical inference consists in inferring a stochastic language, i.e. a probability distribu- tion, in some class of probabilistic models, from a sample of words independently drawn according to a fixed unknown target distribution p. Here we consider the class of rational stochastic languages composed of stochastic languages that can be computed by muliplicity automata, which can be viewed as a generalization of probabilistic automata. Rational stochastic languages p have a useful algebraic characterization: all the mappings up:v-¿p(uv) lie in a finite dimensional vector subspace Vp of the vector space R(E) composed of all real-valued functions defined over E. Hence, a first step in the grammatial inference process can consist in identifying the subspace Vp. In this paper, we study the possibility of using principal component analysis to achieve this task. We provide an inference algorithm which computes an estimate of the target distribution. We prove sometheoreticalpropertiesofthisalgorithmandweprovideresultsfromnumericalsimulationsthatconfirm the relevance of our approach. |
| 关 键 词: | 语法推理; 主成分; 分析问题 |
| 课程来源: | 视频讲座网 |
| 数据采集: | 2025-04-25：liyq |
| 最后编审: | 2025-04-25：liyq |
| 阅读次数: | 8 |