Qiang Wu

48 papers B 1Journal 44Unranked 3
YearRankTypeTitle / Venue / Authors
2023 J jnl
Attention Deficit Hyperactivity Disorder Classification Based on Deep Learning.
IEEE ACM Trans. Comput. Biol. Bioinform.
Donglin Wang, Don Hong, Qiang Wu
2022 J jnl
A statistical learning assessment of Huber regression.
J. Approx. Theory
Yunlong Feng, Qiang Wu
2022 J jnl
Fast Rates of Gaussian Empirical Gain Maximization With Heavy-Tailed Noise.
IEEE Trans. Neural Networks Learn. Syst.
Shouyou Huang, Yunlong Feng, Qiang Wu
2022 J jnl
Optimality of regularized least squares ranking with imperfect kernels.
Inf. Sci.
Fangchao He, Yu Zeng, Lie Zheng, Qiang Wu
2021 J jnl
A Framework of Learning Through Empirical Gain Maximization.
Neural Comput.
Yunlong Feng, Qiang Wu
2021 J jnl
Kernel gradient descent algorithm for information theoretic learning.
J. Approx. Theory
Ting Hu, Qiang Wu, Ding-Xuan Zhou
2021 — conf
On the Selection of Hyperparameters in Convolutional Neural Networks.
CSCI
Donglin Wang, Qiang Wu
2021 J jnl
Optimal Rates of Distributed Regression with Imperfect Kernels.
J. Mach. Learn. Res.
Hongwei Sun, Qiang Wu
2021 — conf
Pairwise Learning for Imbalanced Data Classification.
CSCI
Shu Liu, Qiang Wu
2021 J jnl
Robust pairwise learning with Huber loss.
J. Complex.
Shouyou Huang, Qiang Wu
2020 J jnl
A Framework of Learning Through Empirical Gain Maximization.
CoRR
Yunlong Feng, Qiang Wu
2020 J jnl
A Statistical Learning Assessment of Huber Regression.
CoRR
Yunlong Feng, Qiang Wu
2020 J jnl
Averaging versus voting: A comparative study of strategies for distributed classification.
Math. Found. Comput.
Donglin Wang, Honglan Xu, Qiang Wu
2020 J jnl
Distributed Minimum Error Entropy Algorithms.
J. Mach. Learn. Res.
Xin Guo, Ting Hu, Qiang Wu
2020 J jnl
Modeling interactive components by coordinate kernel polynomial models.
Math. Found. Comput.
Xin Guo, Lexin Li, Qiang Wu
2020 J jnl
Optimal Rates of Distributed Regression with Imperfect Kernels.
CoRR
Hongwei Sun, Qiang Wu
2019 J jnl
Machine learning-based microarray analyses indicate low-expression genes might collectively influence PAH disease.
PLoS Comput. Biol.
Song Cui, Qiang Wu, James West, Jiangping Bai
2019 J jnl
Online learning for supervised dimension reduction.
Math. Found. Comput.
Ning Zhang, Qiang Wu
2018 J jnl
Overlapping Sliced Inverse Regression for Dimension Reduction.
CoRR
Ning Zhang, Zhou Yu, Qiang Wu
2017 B conf
Bias corrected regularization kernel network and its applications.
IJCNN
Qiang Wu
2017 J jnl
Learning Theory of Distributed Regression with Bias Corrected Regularization Kernel Network.
CoRR
Zheng-Chu Guo, Lei Shi, Qiang Wu
2017 J jnl
Learning Theory of Distributed Regression with Bias Corrected Regularization Kernel Network.
J. Mach. Learn. Res.
Zheng-Chu Guo, Lei Shi, Qiang Wu
2017 J jnl
Learning performance of regularized moving least square regression.
J. Comput. Appl. Math.
Hongzhi Tong, Qiang Wu
2016 J jnl
Convergence of Gradient Descent for Minimum Error Entropy Principle in Linear Regression.
IEEE Trans. Signal Process.
Ting Hu, Qiang Wu, Ding-Xuan Zhou
2015 J jnl
A New Approach for Physiological Time Series.
Adv. Data Sci. Adapt. Anal.
Dong Mao, Yang Wang, Qiang Wu
2015 J jnl
A new approach for physiological time series.
CoRR
Dong Mao, Yang Wang, Qiang Wu
2015 J jnl
Sparse Representation in Kernel Machines.
IEEE Trans. Neural Networks Learn. Syst.
Hongwei Sun, Qiang Wu
2014 J jnl
Consistency Analysis of an Empirical Minimum Error Entropy Algorithm.
CoRR
Jun Fan, Ting Hu, Qiang Wu, Ding-Xuan Zhou
2014 J jnl
Multiple Authors Detection: A Quantitative Analysis of Dream of the Red Chamber.
CoRR
Xianfeng Hu, Yang Wang, Qiang Wu
2014 J jnl
Multiple authors Detection: a Quantitative Analysis of Dream of the Red Chamber.
Adv. Data Sci. Adapt. Anal.
Xianfeng Hu, Yang Wang, Qiang Wu
2013 J jnl
Learning theory approach to minimum error entropy criterion.
J. Mach. Learn. Res.
Ting Hu, Jun Fan, Qiang Wu, Ding-Xuan Zhou
2013 J jnl
Regularization networks with indefinite kernels.
J. Approx. Theory
Qiang Wu
2012 J jnl
Empirical Mode Decomposition Analysis for Visual Stylometry.
IEEE Trans. Pattern Anal. Mach. Intell.
James Michael Hughes, Dong Mao, Daniel N. Rockmore, Yang Wang, Qiang Wu
2012 J jnl
Learning Theory Approach to Minimum Error Entropy Criterion
CoRR
Ting Hu, Jun Fan, Qiang Wu, Ding-Xuan Zhou
2012 J jnl
Learning the coordinate gradients.
Adv. Comput. Math.
Yiming Ying, Qiang Wu, Colin Campbell
2012 J jnl
Sparse PCA by iterative elimination algorithm.
Adv. Comput. Math.
Yang Wang, Qiang Wu
2011 J jnl
Estimating variable structure and dependence in multitask learning via gradients.
Mach. Learn.
Justin Guinney, Qiang Wu, Sayan Mukherjee
2010 J jnl
Learning Gradients: Predictive Models that Infer Geometry and Statistical Dependence.
J. Mach. Learn. Res.
Qiang Wu, Justin Guinney, Mauro Maggioni, Sayan Mukherjee
2010 J jnl
Regularized least square regression with dependent samples.
Adv. Comput. Math.
Hongwei Sun, Qiang Wu
2009 J jnl
Application of integral operator for regularized least-square regression.
Math. Comput. Model.
Hongwei Sun, Qiang Wu
2008 J jnl
Learning with sample dependent hypothesis spaces.
Comput. Math. Appl.
Qiang Wu, Ding-Xuan Zhou
2008 — conf
Localized Sliced Inverse Regression.
NIPS
Qiang Wu, Sayan Mukherjee, Feng Liang
2007 J jnl
Characterizing the Function Space for Bayesian Kernel Models.
J. Mach. Learn. Res.
Natesh S. Pillai, Qiang Wu, Feng Liang, Sayan Mukherjee, Robert L. Wolpert
2007 J jnl
Multi-kernel regularized classifiers.
J. Complex.
Qiang Wu, Yiming Ying, Ding-Xuan Zhou
2006 J jnl
Estimation of Gradients and Coordinate Covariation in Classification.
J. Mach. Learn. Res.
Sayan Mukherjee, Qiang Wu
2006 J jnl
Learning Rates of Least-Square Regularized Regression.
Found. Comput. Math.
Qiang Wu, Yiming Ying, Ding-Xuan Zhou
2005 J jnl
SVM Soft Margin Classifiers: Linear Programming versus Quadratic Programming.
Neural Comput.
Qiang Wu, Ding-Xuan Zhou
2004 J jnl
Support Vector Machine Soft Margin Classifiers: Error Analysis.
J. Mach. Learn. Res.
Di-Rong Chen, Qiang Wu, Yiming Ying, Ding-Xuan Zhou