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加权双向匹配的一致结构化估计

Consistent Structured Estimation for Weighted Bipartite Matching
课程网址: http://videolectures.net/aml08_caetano_csewbm/  
主讲教师: Julian McAuley; James Petterson; Tibério Caetano
开课单位: 澳大利亚信息通信技术研究中心
开课时间: 2008-12-20
课程语种: 英语
中文简介:
给出一个加权二部图,其赋值问题包括寻找最重的完全匹配。这是组合优化中的一个经典问题,可以用匈牙利算法等标准方法精确有效地求解,在实际应用中有着广泛的应用。我们给出了分配问题的指数族模型。边缘权重是从边缘特征和参数向量的适当组合中获得的,通过学习这些特征和参数向量,最大限度地提高由训练图及其标记匹配组成的样本的可能性。所得到的一致估计量与现有的最大边际结构估计量进行了比较,两者对于这个问题是不一致的。
课程简介: Given a weighted bipartite graph, the assignment problem consists of finding the heaviest perfect match. This is a classical problem in combinatorial optimization, which is solvable exactly and efficiently by standard methods such as the Hungarian algorithm, and is widely applicable in real-world scenarios. We give an exponential family model for the assignment problem. Edge weights are obtained from a suitable composition of edge features and a parameter vector, which is learned so as to maximize the likelihood of a sample consisting of training graphs and their labeled matches. The resulting consistent estimator contrasts with existing max-margin structured estimators, which are inconsistent for this problem.
关 键 词: 计算机算法; 参数向量; 样本组成
课程来源: 视频讲座网
最后编审: 2021-05-15:yumf
阅读次数: 51