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大缘结构预测采用线性规划

Large-Margin Structured Prediction via Linear Programming
课程网址: http://videolectures.net/smartdw09_wang_lmsp/  
主讲教师: Zhuoran Wang
开课单位: 伦敦大学学院
开课时间: 2009-07-01
课程语种: 英语
中文简介:
本文提出了一种新的结构化分类学习算法,其任务是预测输入对象的多个标签和多标签(多标签)。在正确的多标签和不正确的多标签之间找到大边距分离的问题被表述为线性程序。不是用指数大的约束集明确地写出整个问题,而是通过列生成迭代地求解线性程序。在这种情况下,生成大多数违反约束的过程等同于搜索得分最高的错误分类的不正确多标记,这可以通过基于当前估计解码结构而容易地实现。此外,我们还探索了列生成和线性编程的额外方法的集成,以进一步提高效率。所提出的方法具有可以处理任意结构和更大规模问题的优点。报告了词性标注和统计机器翻译任务的实验结果,证明了我们方法的竞争力。
课程简介: This paper presents a novel learning algorithm for structured classification, where the task is to predict multiple and interacting labels (multilabel) for an input object. The problem of finding a large-margin separation between correct multilabels and incorrect ones is formulated as a linear program. Instead of explicitly writing out the entire problem with an exponentially large constraint set, the linear program is solved iteratively via column generation. In this case, the process of generating most violated constraints is equivalent to searching for highest-scored misclassified incorrect multilabels, which can be easily achieved by decoding the structure based on current estimations. In addition, we also explore the integration of column generation and an extragradient method for linear programming to gain further efficiency. The proposed method has the advantages that it can handle arbitrary structures and larger-scale problems. Experimental results on part-of-speech tagging and statistical machine translation tasks are reported, demonstrating the competitiveness of our approach.
关 键 词: 线性规划; 分类算法; 外梯度方法
课程来源: 视频讲座网
最后编审: 2020-06-29:wuyq
阅读次数: 34