Qiang Wu

According to our database, Qiang Wu authored at least 40 papers between 2004 and 2023.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of three.

2023

Attention Deficit Hyperactivity Disorder Classification Based on Deep Learning.

Donglin Wang, Don Hong, Qiang Wu

IEEE ACM Trans. Comput. Biol. Bioinform., 2023

2022

Fast Rates of Gaussian Empirical Gain Maximization With Heavy-Tailed Noise.

Shouyou Huang, Yunlong Feng, Qiang Wu

IEEE Trans. Neural Networks Learn. Syst., 2022

A statistical learning assessment of Huber regression.

Yunlong Feng, Qiang Wu

J. Approx. Theory, 2022

Optimality of regularized least squares ranking with imperfect kernels.

Fangchao He, Yu Zeng, Lie Zheng, Qiang Wu

Inf. Sci., 2022

2021

A Framework of Learning Through Empirical Gain Maximization.

Yunlong Feng, Qiang Wu

Neural Comput., 2021

Optimal Rates of Distributed Regression with Imperfect Kernels.

Hongwei Sun, Qiang Wu

J. Mach. Learn. Res., 2021

Robust pairwise learning with Huber loss.

Shouyou Huang, Qiang Wu

J. Complex., 2021

Kernel gradient descent algorithm for information theoretic learning.

Ting Hu, Qiang Wu, Ding-Xuan Zhou

J. Approx. Theory, 2021

On the Selection of Hyperparameters in Convolutional Neural Networks.

Donglin Wang, Qiang Wu

Proceedings of the International Conference on Computational Science and Computational Intelligence, 2021

2020

Averaging versus voting: A comparative study of strategies for distributed classification.

Donglin Wang, Honglan Xu, Qiang Wu

Math. Found. Comput., 2020

Modeling interactive components by coordinate kernel polynomial models.

Xin Guo, Lexin Li, Qiang Wu

Math. Found. Comput., 2020

Distributed Minimum Error Entropy Algorithms.

Xin Guo, Ting Hu, Qiang Wu

J. Mach. Learn. Res., 2020

2019

Machine learning-based microarray analyses indicate low-expression genes might collectively influence PAH disease.

Song Cui, Qiang Wu, James West, Jiangping Bai

PLoS Comput. Biol., 2019

Online learning for supervised dimension reduction.

Ning Zhang, Qiang Wu

Math. Found. Comput., 2019

2018

Overlapping Sliced Inverse Regression for Dimension Reduction.

Ning Zhang, Zhou Yu, Qiang Wu

CoRR, 2018

2017

Learning Theory of Distributed Regression with Bias Corrected Regularization Kernel Network.

Zheng-Chu Guo, Lei Shi, Qiang Wu

J. Mach. Learn. Res., 2017

Learning performance of regularized moving least square regression.

Hongzhi Tong, Qiang Wu

J. Comput. Appl. Math., 2017

Bias corrected regularization kernel network and its applications.

Qiang Wu

Proceedings of the 2017 International Joint Conference on Neural Networks, 2017

2016

Convergence of Gradient Descent for Minimum Error Entropy Principle in Linear Regression.

Ting Hu, Qiang Wu, Ding-Xuan Zhou

IEEE Trans. Signal Process., 2016

2015

Sparse Representation in Kernel Machines.

Hongwei Sun, Qiang Wu

IEEE Trans. Neural Networks Learn. Syst., 2015

A New Approach for Physiological Time Series.

Dong Mao, Yang Wang, Qiang Wu

Adv. Data Sci. Adapt. Anal., 2015

2014

Consistency Analysis of an Empirical Minimum Error Entropy Algorithm.

Jun Fan, Ting Hu, Qiang Wu, Ding-Xuan Zhou

CoRR, 2014

Multiple authors Detection: a Quantitative Analysis of Dream of the Red Chamber.

Xianfeng Hu, Yang Wang, Qiang Wu

Adv. Data Sci. Adapt. Anal., 2014

2013

Learning theory approach to minimum error entropy criterion.

Ting Hu, Jun Fan, Qiang Wu, Ding-Xuan Zhou

J. Mach. Learn. Res., 2013

Regularization networks with indefinite kernels.

Qiang Wu

J. Approx. Theory, 2013

2012

Empirical Mode Decomposition Analysis for Visual Stylometry.

James Michael Hughes, Dong Mao, Daniel N. Rockmore, Yang Wang, Qiang Wu

IEEE Trans. Pattern Anal. Mach. Intell., 2012

Learning the coordinate gradients.

Yiming Ying, Qiang Wu, Colin Campbell

Adv. Comput. Math., 2012

Sparse PCA by iterative elimination algorithm.

Yang Wang, Qiang Wu

Adv. Comput. Math., 2012

2011

Estimating variable structure and dependence in multitask learning via gradients.

Justin Guinney, Qiang Wu, Sayan Mukherjee

Mach. Learn., 2011

2010

Learning Gradients: Predictive Models that Infer Geometry and Statistical Dependence.

Qiang Wu, Justin Guinney, Mauro Maggioni, Sayan Mukherjee

J. Mach. Learn. Res., 2010

Regularized least square regression with dependent samples.

Hongwei Sun, Qiang Wu

Adv. Comput. Math., 2010

2009

Application of integral operator for regularized least-square regression.

Hongwei Sun, Qiang Wu

Math. Comput. Model., 2009

2008

Learning with sample dependent hypothesis spaces.

Qiang Wu, Ding-Xuan Zhou

Comput. Math. Appl., 2008

Localized Sliced Inverse Regression.

Qiang Wu, Sayan Mukherjee, Feng Liang

Proceedings of the Advances in Neural Information Processing Systems 21, 2008

2007

Characterizing the Function Space for Bayesian Kernel Models.

Natesh S. Pillai, Qiang Wu, Feng Liang, Sayan Mukherjee, Robert L. Wolpert

J. Mach. Learn. Res., 2007

Multi-kernel regularized classifiers.

Qiang Wu, Yiming Ying, Ding-Xuan Zhou

J. Complex., 2007

2006

Estimation of Gradients and Coordinate Covariation in Classification.

Sayan Mukherjee, Qiang Wu

J. Mach. Learn. Res., 2006

Learning Rates of Least-Square Regularized Regression.

Qiang Wu, Yiming Ying, Ding-Xuan Zhou

Found. Comput. Math., 2006

2005

SVM Soft Margin Classifiers: Linear Programming versus Quadratic Programming.

Qiang Wu, Ding-Xuan Zhou

Neural Comput., 2005

2004

Support Vector Machine Soft Margin Classifiers: Error Analysis.

Di-Rong Chen, Qiang Wu, Yiming Ying, Ding-Xuan Zhou

J. Mach. Learn. Res., 2004