Multilabel relationship learning

By Yu Zhang, Dit-Yan Yeung. 2013

59 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Computer Vision and Pattern Recognition, Evaluation & Benchmarks

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

1. Qiang Yang
2. Yu Zhang
3. Yu Zhang
4. Mingsheng Long
5. Jianmin Wang
6. Sinno Jialin Pan
7. Dongrui Wu
8. Sebastian Ruder

In Computer Vision and Pattern Recognition

1. Qiang Yang
2. Yu Zhang
3. Yu Zhang
4. Gabriella Csurka
5. Sinno Jialin Pan
6. Dacheng Tao
7. Michael R. Lyu
8. Steffen Rendle

In Evaluation & Benchmarks

1. Qiang Yang
2. Yu Zhang
3. Yu Zhang

Citations in the corpus

• An Overview of Multi-task Learning (2018, methodology): “The MTRL method is generalized to multi-task boosting [46] and multi-label learning [47], where each label is treated as a task, and extended to learn sparse task relations in [48].”
• A Survey on Multi-Task Learning (2017, background): “Problem (21) has been extended to multi-task boosting [83] and multi-label learning [84] by learning label correlations.”
• Parallel Multi-task Learning (2015, background): “Experiments on several benchmark datasets show that the convergence of the designed algorithms is as fast as that of nonparallel sequential minimal optimization (SMO) algorithms proposed in [27], [16] for multi-task learning and the parallel design can speedup the computation.”