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- W3114610081 abstract "Complete information about the class label is not given in many real-world classification problems. For example in a multiclass setting, instead of the ground-truth label, we can be given a set of candidate labels, assuming that the true label belongs to this set. This type of setting is called learning under partial labels. In this paper, we propose exact passive-aggressive online algorithms for multiclass classification using only partial labels. For updating the weights, we find the exact solution of a quadratic optimization problem under multiple class separability constraints. We obtain this by finding the active constraints using KKT conditions of the optimization problem. The set of support classes for which the weight vector is to be updated is determined by these constraints. The proposed algorithms are called PA, PA-I, and PA-II. We also provide a thorough theoretical analysis of the proposed algorithms including regret bounds. We provide extensive simulation results to show the effectiveness of the proposed approaches." @default.
- W3114610081 created "2021-01-05" @default.
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- W3114610081 date "2021-01-02" @default.
- W3114610081 modified "2023-09-26" @default.
- W3114610081 title "Exact Passive Aggressive Algorithm for Multiclass Classification Using Partial Labels" @default.
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- W3114610081 doi "https://doi.org/10.1145/3430984.3431010" @default.
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