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按排名学习差异：从SDP到QP

Learning Dissimilarities by Ranking: From SDP to QP
课程网址: http://videolectures.net/icml08_ouyang_ldr/  
主讲教师: Hua Ouyang
开课单位: 佐治亚理工学院
开课时间: 2008-08-29
课程语种: 英语
中文简介:
我们考虑通过在点之间保持指定排序而不是相异度的数值的公式来学习点之间的不相似性的问题。不相似性排序（d排名）从诸如“A更类似于B而不是C与D”或“E和F之间的距离大于G和H之间的距离”的实例中学习。给出了三个d排序问题的公式，并给出了其中两个的新算法，一个是半定规划（SDP），另一个是二次规划（QP）。这些方法的新功能包括样本预测和大问题的可扩展性。
课程简介: We consider the problem of learning dissimilarities between points via formulations which preserve a specified ordering between points rather than the numerical values of the dissimilarities. Dissimilarity ranking (d-ranking) learns from instances like "A is more similar to B than C is to D" or "The distance between E and F is larger than that between G and H". Three formulations of d-ranking problems are presented and new algorithms are presented for two of them, one by semidefinite programming (SDP) and one by quadratic programming (QP). Among the novel capabilities of these approaches are out-of-sample prediction and scalability to large problems.
关 键 词: 指定排序; 不相似性排序; d排序
课程来源: 视频讲座网
最后编审: 2019-04-19：lxf
阅读次数: 93

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