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Auxiliary Weight Normalization and Relaxation Paths for Learning Uncertain Data
In this paper, we propose a new approach to nonlinearly smooth multi-label classification with adaptive clustering that achieves similar performance and generalization guarantees as the classic multi-label learning on both unsupervised and supervised datasets, and significantly improves the generalization error for these two datasets. We then provide a generic algorithm for learning to distinguish between three classes of data points, which we call the three-class class clustering (3CL). A 3CL is a supervised classifier that is not only unbiased but also adaptable to the environment. We show that for 3CL to provide a more accurate classification performance, we need to learn the discriminative class of data points, which is the first step in training a 3CL. Moreover, we show that learning 3CL with regularizing rule improves classification accuracy for the same classification task.
In this paper, we propose a new approach to nonlinearly smooth multi-label classification with adaptive clustering that achieves similar performance and generalization guarantees as the classic multi-label learning on both unsupervised and supervised datasets, and significantly improves the generalization error for these two datasets. We then provide a generic algorithm for learning to distinguish between three classes of data points, which we call the three-class class clustering (3CL). A 3CL is a supervised classifier that is not only unbiased but also adaptable to the environment. We show that for 3CL to provide a more accurate classification performance, we need to learn the discriminative class of data points, which is the first step in training a 3CL. Moreover, we show that learning 3CL with regularizing rule improves classification accuracy for the same classification task.