no code implementations • 6 Apr 2021 • Min Feng, Feng Gao, Jian Fang, Junyu Dong
An efficient linear self-attention fusion model is proposed in this paper for the task of hyperspectral image (HSI) and LiDAR data joint classification.
no code implementations • 15 Oct 2020 • Jiahui Wen, Jingwei Ma, Hongkui Tu, Wei Yin, Jian Fang
At review level, we mutually propagate textual features between the user and item, and capture the informative reviews.
no code implementations • 22 Mar 2018 • Jian Fang, Shao-Bo Lin, Zongben Xu
Supervised learning frequently boils down to determining hidden and bright parameters in a parameterized hypothesis space based on finite input-output samples.
no code implementations • 18 Sep 2014 • Jian Fang, Shao-Bo Lin, Zongben Xu
We consider the approximation capability of orthogonal super greedy algorithms (OSGA) and its applications in supervised learning.
no code implementations • 24 Jan 2014 • Shaobo Lin, Xia Liu, Jian Fang, Zongben Xu
On one hand, we find that the randomness causes an additional uncertainty problem of ELM, both in approximation and learning.
no code implementations • 19 Dec 2013 • Shaobo Lin, Jinshan Zeng, Jian Fang, Zongben Xu
Regularization is a well recognized powerful strategy to improve the performance of a learning machine and $l^q$ regularization schemes with $0<q<\infty$ are central in use.
no code implementations • 27 Oct 2013 • Jian Fang, Zongben Xu, Bingchen Zhang, Wen Hong, Yirong Wu
Multilook processing is a widely used speckle reduction approach in synthetic aperture radar (SAR) imaging.
no code implementations • 25 Jul 2013 • Shaobo Lin, Chen Xu, Jingshan Zeng, Jian Fang
To facilitate the use of $l^{q}$-regularization, we intend to seek for a modeling strategy where an elaborative selection on $q$ is avoidable.