Results 221 to 230 of about 273,362 (259)

Multi-label Learning by Exploiting Label Correlations with LDA

2017 IEEE 29th International Conference on Tools with Artificial Intelligence (ICTAI), 2017
In multi-label learning, each object is represented by a single instance while associated with a set of class labels, and labels often have correlations with each other. Exploiting label correlations can improve the performances of classifiers. Current multi-label classification methods mainly consider the correlations from label pairwise or label ...
Yue Peng, Gang Chen, Ming Xu 0014, Chongjun Wang, Junyuan Xie   +4 more
openaire   +1 more source

Label distribution learning with correlation information

Engineering Applications of Artificial Intelligence
Weiping Ding, Yaojin Lin, Wu Yilin
exaly   +2 more sources

Generative Multi-Label Correlation Learning

ACM Transactions on Knowledge Discovery from Data, 2023
In real-world applications, a single instance could have more than one label. To solve this task, multi-label learning methods emerged in recent years. It is a more challenging problem for many reasons, such as complex label correlation, long-tail label distribution, and data shortage.
Lichen Wang, Zhengming Ding, Kasey Lee, Seungju Han 0001, Jae-Joon Han, Changkyu Choi, Yun Fu 0001   +6 more
openaire   +1 more source

Clustered intrinsic label correlations for multi-label classification

Expert Systems with Applications, 2017
The classifier for each label consists of a label-specific part and a shared one.The label-specific part characterizes the corresponding label.The shared part represents the information shared by all labels.Intrinsic label correlations are represented by label-specific parts.The proposed method extends SVM to the multi-label setting.
Jujie Zhang, Min Fang, Xiao Li 0008
openaire   +1 more source

Multi-label Classification with Label Correlations of Multimedia Datasets

2016
In multi-label classification tasks, very often labels are correlated and to not lose important information, methods should take into account existing dependencies. Such situation especially takes place in the case of multimedia datasets. In the paper, universal problem transformation methods providing for label correlations are considered.
Kinga Glinka, Danuta Zakrzewska
openaire   +1 more source

Multi-Label Deep Active Learning with Label Correlation

2018 25th IEEE International Conference on Image Processing (ICIP), 2018
Annotating a data sample in a multi-label learning problem requires a human oracle to consider the presence/absence of every possible label separately, which is extremely labor intensive. Active learning algorithms automatically identify the informative samples from large amounts of unlabeled data and significantly reduce human annotation efforts in ...
Hiranmayi Ranganathan, Hemanth Venkateswara, Shayok Chakraborty, Sethuraman Panchanathan   +3 more
openaire   +1 more source

Joint label-specific features and label correlation for multi-label learning with missing label

Applied Intelligence, 2020
Existing multi-label learning classification algorithms ignore that class labels may be determined by some features in the original feature space. And only a partial label of each instance can be obtained for some real applications. Therefore, we propose a novel algorithm named joint Label-Specific features and Label Correlation for multi-label ...
Ziwei Cheng, Ziwei Zeng
openaire   +1 more source

Multi-label learning with label-specific features by resolving label correlations

Knowledge-Based Systems, 2018
Abstract In multi-label learning, different labels may have their own inherent characteristics for distinguishing each other, in the meanwhile, exploiting the correlations among labels is another practical yet challenging task to improve the performance. In this work, we present a new method for the joint learning of label-specific features and label
Jia Zhang 0019, Candong Li, Donglin Cao, Yaojin Lin, Songzhi Su, Liang Dai, Shaozi Li   +6 more
openaire   +1 more source

The correlation between Label Messages and Labeling Effectiveness

2010 International Conference on Science and Social Research (CSSR 2010), 2010
Labeling can perform many different functions, like the identification, description or promotion of food products, however based on research the main purpose of food labeling is to inform consumers on the content and the nutrients of the food. All food labels need to have minimum amount of mandatory or legally set information, but a producer may add ...
Maznah Wan Omar, null Mohd Noor Mohd Ali, null Azfahanee Zakaria, null Syed Ahmad AlHady   +3 more
openaire   +1 more source

Correlated multi-label feature selection

Proceedings of the 20th ACM international conference on Information and knowledge management, 2011
Multi-label learning studies the problem where each instance is associated with a set of labels. There are two challenges in multi-label learning: (1) the labels are interdependent and correlated, and (2) the data are of high dimensionality. In this paper, we aim to tackle these challenges in one shot.
Quanquan Gu, Zhenhui Li, Jiawei Han 0001
openaire   +1 more source