Results 231 to 240 of about 101,283 (263)

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

Label Distribution Learning by Exploiting Fuzzy Label Correlation

IEEE Transactions on Neural Networks and Learning Systems
Researchers have proposed to exploit label correlation to alleviate the exponential-size output space of label distribution learning (LDL). In particular, some have designed LDL methods to consider local label correlation. These methods roughly partition the training set into clusters and then exploit local label correlation on each one.
Jing Wang 0113, Zhiqiang Kou, Yuheng Jia, Jianhui Lv, Xin Geng 0001   +4 more
openaire   +2 more sources

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

Ki67 Labeling Correlated With Invasion But Not With Recurrence

Applied Immunohistochemistry & Molecular Morphology, 2017
Pituitary adenomas account for 10% to 15% of intracranial neoplasms. Multiple factors had been introduced for tumor recurrence. MIB-1 monoclonal antibody, a marker of the proliferative index, has been introduced in various tumors, but unfortunately, the usefulness of MIB-1 in predicting the behavior of pituitary adenoma has been debated recently. Hence,
Alireza, Sadeghipour, Leily, Mahouzi, Mohammad M, Salem, Shaghayegh, Ebrahim-Nejad, Mohsen, Asadi-Lari, Amir, Radfar, Irina, Filip, Pegah, Babaheidarian   +7 more
openaire   +2 more sources

Learning Low-Rank Label Correlations for Multi-label Classification with Missing Labels

2014 IEEE International Conference on Data Mining, 2014
Multi-label learning deals with the problem where each training example is associated with a set of labels simultaneously, with the set of labels corresponding to multiple concepts or semantic meanings. Intuitively, the multiple labels are usually correlated in some semantic space while sharing the same input space.
Linli Xu, Zhen Wang 0004, Zefan Shen, Yubo Wang, Enhong Chen   +4 more
openaire   +1 more source

Multi-label classification with weak labels by learning label correlation and label regularization

Applied Intelligence, 2023
Xiaowan Ji, Anhui Tan, Wei-Zhi Wu 0001, Shenming Gu   +3 more
openaire   +1 more source

Music Recommendation Based on Label Correlation

2013
The Web is becoming the largest source of digital music, and users often find themselves exposed to a huge collection of items. How to effectively help users explore through massive music items creates a significant challenge that must be properly addressed in the era of E-Commerce.
Hequn Liu, Bo Yuan 0003, Cheng Li
openaire   +1 more source

Multi-label Learning By exploiting Correlations of Label Subsets

2021 The 9th International Conference on Information Technology: IoT and Smart City, 2021
Liwen Peng, Xiaolin Zhu, Zhang Yun
openaire   +1 more source

Label Correlation Propagation for Semi-supervised Multi-label Learning

2017
Many real world machine learning tasks suffer from the problem of scarce labeled data. In multi-label learning, each instance is associated with more than one label as in semantic scene understanding, text categorization and bio-informatics. Semi-supervised multi-label learning has attracted recent interest as gathering labeled data is both expensive ...
Aritra Ghosh 0002, C. Chandra Sekhar
openaire   +1 more source