图 书 馆
有效地逼近马尔可夫树袋装以进行高维密度估计Efficiently approximating Markov tree bagging for high-dimensional density estimation |
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| 课程网址: | http://videolectures.net/ecmlpkdd2011_schnitzler_efficiently/ |
| 主讲教师: | François Schnitzler |
| 开课单位: | 列日大学 |
| 开课时间: | 2011-10-03 |
| 课程语种: | 英语 |
| 中文简介: |  |
| 课程简介: | We consider algorithms for generating Mixtures of Bagged Markov Trees, for density estimation. In problems defined over many variables and when few observations are available, those mixtures generally outperform a single Markov tree maximizing the data likelihood, but are far more expensive to compute. In this paper, we describe new algorithms for approximating such models, with the aim of speeding up learning without sacrificing accuracy. More specifically, we propose to use a filtering step obtained as a by-product from computing a first Markov tree, so as to avoid considering poor candidate edges in the subsequently generated trees. We compare these algorithms (on synthetic data sets) to Mixtures of Bagged Markov Trees, as well as to a single Markov tree derived by the classical Chow-Liu algorithm and to a recently proposed randomized scheme used for building tree mixtures. |
| 关 键 词: | 变量定义; 马尔可夫树; 随机化方案 |
| 课程来源: | 视频讲座网 |
| 最后编审: | 2019-04-03：lxf |
| 阅读次数: | 73 |