Results 141 to 150 of about 637,915 (182)
Some of the next articles are maybe not open access.
Proceedings of the AAAI Conference on Artificial Intelligence, 2018
Embedding methods have shown promising performance in multi-label prediction, as they can discover the dependency of labels. Most embedding methods cannot well align the input and output, which leads to degradation in prediction performance.
Xiaobo Shen +4 more
openaire +1 more source
Embedding methods have shown promising performance in multi-label prediction, as they can discover the dependency of labels. Most embedding methods cannot well align the input and output, which leads to degradation in prediction performance.
Xiaobo Shen +4 more
openaire +1 more source
Proceedings of the AAAI Conference on Artificial Intelligence, 2016
This paper gives an attempt to explore the manifold in the label space for multi-label learning. Traditional label space is logical, where no manifold exists. In order to study the label manifold, the label space should be extended to a Euclidean space.
Peng Hou, Xin Geng, Min-Ling Zhang
openaire +1 more source
This paper gives an attempt to explore the manifold in the label space for multi-label learning. Traditional label space is logical, where no manifold exists. In order to study the label manifold, the label space should be extended to a Euclidean space.
Peng Hou, Xin Geng, Min-Ling Zhang
openaire +1 more source
Proceedings of the AAAI Conference on Artificial Intelligence, 2018
It is expensive and difficult to precisely annotate objects with multiple labels. Instead, in many real tasks, annotators may roughly assign each object with a set of candidate labels. The candidate set contains at least one but unknown number of ground-truth labels, and is usually adulterated with some irrelevant labels. In this paper,
Ming-Kun Xie, Sheng-Jun Huang
openaire +1 more source
It is expensive and difficult to precisely annotate objects with multiple labels. Instead, in many real tasks, annotators may roughly assign each object with a set of candidate labels. The candidate set contains at least one but unknown number of ground-truth labels, and is usually adulterated with some irrelevant labels. In this paper,
Ming-Kun Xie, Sheng-Jun Huang
openaire +1 more source
Discriminative Multi-label Model Reuse for Multi-label Learning
2020Traditional Chinese Medicine (TCM) with diagnosis scales is a holistic way for diagnosing Parkinson’s Disease, where symptoms can be represented as multiple labels. To solve this problem, multi-label learning provides a framework for handling such task and has exhibited excellent performance.
Yi Zhang +4 more
openaire +1 more source
2022
Research on multi-label classification is concerned with developing and evaluating algorithms that learn a predictive model for the automatic assignment of data points to a subset of predefined class labels. This is in contrast to traditional classification settings, where individual data points cannot be assigned to more than a single class.
openaire +1 more source
Research on multi-label classification is concerned with developing and evaluating algorithms that learn a predictive model for the automatic assignment of data points to a subset of predefined class labels. This is in contrast to traditional classification settings, where individual data points cannot be assigned to more than a single class.
openaire +1 more source
Long Tail Multi-label Learning
2019 IEEE Second International Conference on Artificial Intelligence and Knowledge Engineering (AIKE), 2019Multi-label learning is an activity research area that many methods arise recently to solve this problem. However, according to the results of current researches, the class imbalance which appears in the most of labels makes the network unable to be trained.
Mengqi Yuan, Jinke Xu, Zhongnian Li
openaire +1 more source
Robust Extreme Multi-label Learning
Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 2016Tail labels in the multi-label learning problem undermine the low-rank assumption. Nevertheless, this problem has rarely been investigated. In addition to using the low-rank structure to depict label correlations, this paper explores and exploits an additional sparse component to handle tail labels behaving as outliers, in order to make the classical ...
Chang Xu, Dacheng Tao, Chao Xu
openaire +1 more source
Causal multi-label learning for image classification
Neural Networks, 2023In this paper, we investigate the problem of causal image classification with multi-label learning. As multi-label learning involves a diversity of supervision signals, it is considered a challenging issue to solve. Previous approaches have attempted to improve performance by identifying label-related image areas or exploiting the co-occurrence of ...
Yingjie Tian +3 more
openaire +2 more sources
Multi-Label Learning Via Codewords
2018 IEEE 30th International Conference on Tools with Artificial Intelligence (ICTAI), 2018In this paper, we introduce a novel hash learning framework for multi-label learning which employs structured prediction. A hash function is learned to embed samples in Hamming spaces, and for each label, a pair of codewords are simultaneously inferred from the available data.
Sedghi, Mahlagha +3 more
openaire +2 more sources
Towards Class-Imbalance Aware Multi-Label Learning
IEEE Transactions on Cybernetics, 2022Multi-label learning deals with training examples each represented by a single instance while associated with multiple class labels. Due to the exponential number of possible label sets to be considered by the predictive model, it is commonly assumed that label correlations should be well exploited to design an effective multi-label learning approach ...
Min-Ling Zhang +3 more
openaire +2 more sources