5 citations · 5 across the 2 of their papers we have counts for
6 papers · 1 filter
Construction of neural networks for realization of localized deep learning
Charles K. Chui, Shao-Bo Lin, Ding-Xuan Zhou
The subject of deep learning has recently attracted users of machine learning from various disciplines, including: medical diagnosis and bioinformatics, financial market analysis a…
Learning through deterministic assignment of hidden parameters
Jian Fang, Shaobo 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. The hidden parameter…
Constructive neural network learning
Shaobo Lin, Jinshan Zeng, Xiaoqin Zhang
In this paper, we aim at developing scalable neural network-type learning systems. Motivated by the idea of "constructive neural networks" in approximation theory, we focus on "con…
Greedy Criterion in Orthogonal Greedy Learning
Lin Xu, Shaobo Lin, Jinshan Zeng +2
Orthogonal greedy learning (OGL) is a stepwise learning scheme that starts with selecting a new atom from a specified dictionary via the steepest gradient descent (SGD) and then bu…
Shrinkage degree in -re-scale boosting for regression
Lin Xu, Shaobo Lin, Yao Wang +1
Re-scale boosting (RBoosting) is a variant of boosting which can essentially improve the generalization performance of boosting learning. The key feature of RBoosting lies in intro…
Re-scale boosting for regression and classification
Shaobo Lin, Yao Wang, Lin Xu
Boosting is a learning scheme that combines weak prediction rules to produce a strong composite estimator, with the underlying intuition that one can obtain accurate prediction rul…