结构化预测研究进展Advances in Structured Prediction |
|
课程网址: | http://videolectures.net/icml2015_daume_structured_prediction/ |
主讲教师: | Hal Daumé III |
开课单位: | 马里兰大学 |
开课时间: | 2015-12-05 |
课程语种: | 英语 |
中文简介: | 结构化预测是一个联合决策集以优化联合损失的问题。针对这类问题有两类算法:图形模型方法和学习搜索方法。图形模型包括条件随机场和结构化支持向量机,它们在写出图形模型且求解容易的情况下是有效的。学习搜索方法,明确地预测联合决策集增量,在过去和未来的决策条件。当预测之间的依赖关系很复杂,损失很复杂,或者不可能构建明确的图形模型时,这种模型可能特别有用。我们将描述这两种方法,更深入地关注后一种学习搜索范式,它没有太多的教程支持。在过去的五年中,这种范式得到了越来越多的关注,在自然语言处理(依赖解析、语义解析)、机器人(抓取和路径规划)、社交网络分析和计算机视觉(对象分割)方面取得了进展。 |
课程简介: | Structured prediction is the problem of making a joint set of decisions to optimize a joint loss. There are two families of algorithms for such problems: Graphical model approaches and learning to search approaches. Graphical models include Conditional Random Fields and Structured SVMs and are effective when writing down a graphical model and solving it is easy. Learning to search approaches, explicitly predict the joint set of decisions incrementally, conditioning on past and future decisions. Such models may be particularly useful when the dependencies between the predictions are complex, the loss is complex, or the construction of an explicit graphical model is impossible. We will describe both approaches, with a deeper focus on the latter learning-to-search paradigm, which has less tutorial support. This paradigm has been gaining increasing traction over the past five years, making advances in natural language processing (dependency parsing, semantic parsing), robotics (grasping and path planning), social network analysis and computer vision (object segmentation). |
关 键 词: | 图形模型; 学习搜索; 自然语言 |
课程来源: | 视频讲座网 |
数据采集: | 2023-04-20:chenxin01 |
最后编审: | 2024-01-22:liyy |
阅读次数: | 41 |