图 书 馆
对数线性模型和条件随机场Log-linear Models and Conditional Random Fields |
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| 课程网址: | http://videolectures.net/cikm08_elkan_llmacrf/ |
| 主讲教师: | Charles Elkan |
| 开课单位: | 加州大学圣地亚哥分校 |
| 开课时间: | 2008-11-19 |
| 课程语种: | 英语 |
| 中文简介: |  |
| 课程简介: | Log-linear models are a far-reaching extension of logistic regression, while con- ditional random fields (CRFs) are a special case of log-linear models suitable for so-called structured learning tasks. Structured learning means learning to predict outputs that have internal structure. For example, recognizing handwritten words is more accurate when the correlations between neighboring letters are used to reÞne predictions. This tutorial will provide a simple but thorough introduction to these new developments in machine learning that have great potential for many novel applications. The tutorial will first explain what log-linear models are, with with concrete examples but also with mathematical generality. Next, feature-functions will be explained; these are the knowledge-representation technique underlying log-linear models. The tutorial will then present linear-chain CRFs, from the point of view that they are a special case of log-linear models. The Viterbi algorithm that makes inference tractable for linear-chain CRFs will be covered, followed by a discus- sion of inference for general CRFs. The presentation will continue with a general derivation of the gradient of log-linear models; this is the mathematical foundation of all log-linear training algorithms. Then, the tutorial will discuss two impor- tant special-case CRF training algorithms, one that is a variant of the perceptron method, and another one called contrastive divergence. Last but not least, the tu- torial will introduce publicly available software for training and using CRFs, and will explain a practical application of CRFs with hands-on detail. |
| 关 键 词: | 计算机科学; 对数线性模型; 逻辑回归; 条件随机场; 特征函数; CRF训练算法 |
| 课程来源: | 视频讲座网 |
| 最后编审: | 2019-10-17：cwx |
| 阅读次数: | 23 |