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Learning from Weakly Labeled Data

Learning from Weakly Labeled Data

This video was recorded at International Workshop on Advances in Regularization, Optimization, Kernel Methods and Support Vector Machines (ROKS): theory and applications, Leuven 2013. In many machine learning problems, the labels of the training examples are incomplete. These include, for example, (i) semi-supervised learning where labels are partially known; (ii) multi-instance learning where labels are implicitly known; and (iii) clustering where labels are completely unknown. In this talk, focusing on the SVM as the learner, I will describe a label generation strategy that leads to a convex relaxation of the underlying mixed integer programming problem. Computationally, it can be solved via a sequence of SVM subproblems that are much more scalable than other convex SDP relaxations.... Show More

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