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Label Distribution Learning
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This paper introduces a novel learning paradigm named label distribution learning (LDL) for applications where the importance of the labels matters. It proposes six LDL algorithms and evaluates them on various datasets.
This paper proposes a learning framework that considers the importance of multiple labels for each instance, called label distribution learning (LDL). It introduces six LDL algorithms and evaluates their performance on various datasets.
Label distribution learning (LDL) is a recent hot topic, in which ambiguity is modeled via description degrees of the labels. However, in common LDL tasks, e.g., age esti-mation, labels are in an intrinsic order. The conventional LDL paradigm adopts a per-label manner for optimization,
This paper proposes a novel learning paradigm named label distribution learning (LDL) for such kind of applications. The label distribution covers a certain number of labels, representing the degree to which each label describes the instance. LDL is a more general learning framework which includes both single-label and
A paper that introduces a novel learning paradigm for applications where the importance of labels matters. It proposes six algorithms and six evaluation measures for label distribution learning, and provides a collection of label distribution datasets.
Label Distribution Learning Xin Geng*, and Quan Zhao Abstract—Although multi-label learning can deal with many problems with label ambiguity, it does not fit some real applications well where the overall distribution of the importance of the labels matters. This paper proposes a novel learning paradigm named label
A novel LDL method that captures the continuous distribution of different labels explicitly and effectively. The method learns the distribution in the latent space and extracts the high-order correlations among labels for various real-world tasks.
Label distribution learning (LDL) is a novel machine learning paradigm to deal with label ambiguity issues by placing more emphasis on how relevant each label is to a particular instance. Many LDL algorithms have been proposed and most of them concentrate on the learning models, while few of them focus on the feature selection problem. ...
This article proposes a novel algorithm for label distribution learning (LDL) by introducing the ranking loss function to maintain the label ranking relation. The article also evaluates the LDL algorithms using two ranking metrics and shows the experimental results on 13 real-world datasets.
Learn about label distribution learning (LDL), a novel machine learning paradigm that covers single-label and multi-label learning. Find papers, algorithms, data sets and applications of LDL for facial age estimation, head pose estimation, movie prediction and more.