图 书 馆
对数线性模型和条件随机领域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: | 2008-11-09 |
| 课程语种: | 英语 |
| 中文简介: |  对数线性模型是对数回归的深远延伸，而条件随机字段（CRF）是对数线性模型的一种特殊情况，适用于所谓的结构化学习任务。结构化学习意味着学习预测具有内部结构的输出。例如，当使用相邻字母之间的相关性定义预测时，识别手写单词会更加准确。本教程将对机器学习的这些新发展进行简单而透彻的介绍，这些新发展对于许多新颖的应用程序都具有巨大的潜力。本教程将首先通过具体示例以及数学通用性说明什么是对数线性模型。接下来，将说明特征功能。这些是基于对数线性模型的知识表示技术。然后，本教程将介绍线性链CRF，因为它们是对数线性模型的特例。将讨论使线性链CRF易于推理的Viterbi算法，然后讨论通用CRF的推理。该演示将继续对数线性模型的梯度的一般推导。这是所有对数线性训练算法的数学基础。然后，本教程将讨论两种重要的特殊情况CRF训练算法，一种是感知器方法的变体，另一种称为对比发散。最后但并非最不重要的是，该教程将介绍用于培训和使用CRF的公开可用软件，并详细介绍CRF的实际应用。 |
| 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: | 对数线性模型; 随机字段; CRF; CRF训练算法; 感知器; 对比发散 |
| 课程来源: | 视频讲座网 |
| 数据采集: | 2020-03-27：zhouxj |
| 最后编审: | 2020-05-16：杨雨(课程编辑志愿者) |
| 阅读次数: | 65 |