加权双向匹配的一致结构化估计Consistent Structured Estimation for Weighted Bipartite Matching |
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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 |