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2020 – today
- 2025
[j84]Langming Liu, Ding-Xuan Zhou
:
Analysis of regularized federated learning. Neurocomputing 611: 128579 (2025)- 2024
- [j83]Puyu Wang, Yunwen Lei, Yiming Ying, Ding-Xuan Zhou:
Differentially private stochastic gradient descent with low-noise. Neurocomputing 585: 127557 (2024) - [j82]Yuqing Liu, Tong Mao, Ding-Xuan Zhou:
Approximation of functions from Korobov spaces by shallow neural networks. Inf. Sci. 670: 120573 (2024) - [j81]Jianfei Li, Han Feng, Ding-Xuan Zhou:
SignReLU neural network and its approximation ability. J. Comput. Appl. Math. 440: 115551 (2024) - [j80]Zihan Zhang, Lei Shi, Ding-Xuan Zhou:
Classification with Deep Neural Networks and Logistic Loss. J. Mach. Learn. Res. 25: 125:1-125:117 (2024) - [j79]Yunfei Yang, Ding-Xuan Zhou:
Nonparametric Regression Using Over-parameterized Shallow ReLU Neural Networks. J. Mach. Learn. Res. 25: 165:1-165:35 (2024) - [j78]Shao-Bo Lin, Di Wang, Ding-Xuan Zhou:
Sketching with Spherical Designs for Noisy Data Fitting on Spheres. SIAM J. Sci. Comput. 46(1): 313- (2024) - [j77]Zhan Yu, Jun Fan, Zhongjie Shi, Ding-Xuan Zhou:
Distributed Gradient Descent for Functional Learning. IEEE Trans. Inf. Theory 70(9): 6547-6571 (2024) - [i50]Zhongjie Shi, Jun Fan, Linhao Song, Ding-Xuan Zhou, Johan A. K. Suykens:
Nonlinear functional regression by functional deep neural network with kernel embedding. CoRR abs/2401.02890 (2024) - [i49]Yunfei Yang, Han Feng, Ding-Xuan Zhou:
On the rates of convergence for learning with convolutional neural networks. CoRR abs/2403.16459 (2024) - [i48]Junyu Zhou, Puyu Wang, Ding-Xuan Zhou:
Generalization analysis with deep ReLU networks for metric and similarity learning. CoRR abs/2405.06415 (2024) - [i47]Guangrui Yang, Jianfei Li, Ming Li, Han Feng, Ding-Xuan Zhou:
Bridging Smoothness and Approximation: Theoretical Insights into Over-Smoothing in Graph Neural Networks. CoRR abs/2407.01281 (2024) - [i46]Jianfei Li, Han Feng, Ding-Xuan Zhou:
Convergence Analysis for Deep Sparse Coding via Convolutional Neural Networks. CoRR abs/2408.05540 (2024) - [i45]Zhenyu Yang, Shuo Huang, Han Feng, Ding-Xuan Zhou:
Spherical Analysis of Learning Nonlinear Functionals. CoRR abs/2410.01047 (2024) - 2023
- [j76]Zhan Yu, Ding-Xuan Zhou:
Deep learning theory of distribution regression with CNNs. Adv. Comput. Math. 49(4): 51 (2023) - [j75]Tong Mao, Ding-Xuan Zhou:
Rates of approximation by ReLU shallow neural networks. J. Complex. 79: 101784 (2023) - [j74]Le-Yin Wei, Zhan Yu, Ding-Xuan Zhou:
Federated learning for minimizing nonsmooth convex loss functions. Math. Found. Comput. 6(4): 753-770 (2023) - [j73]Shuo Huang, Junyu Zhou, Han Feng, Ding-Xuan Zhou:
Generalization Analysis of Pairwise Learning for Ranking With Deep Neural Networks. Neural Comput. 35(6): 1135-1158 (2023) - [j72]Linhao Song, Ying Liu, Jun Fan, Ding-Xuan Zhou:
Approximation of smooth functionals using deep ReLU networks. Neural Networks 166: 424-436 (2023) - [j71]Han Feng, Shuo Huang, Ding-Xuan Zhou:
Generalization Analysis of CNNs for Classification on Spheres. IEEE Trans. Neural Networks Learn. Syst. 34(9): 6200-6213 (2023) - [c7]Yunwen Lei, Tianbao Yang, Yiming Ying, Ding-Xuan Zhou:
Generalization Analysis for Contrastive Representation Learning. ICML 2023: 19200-19227 - [i44]Yunwen Lei, Tianbao Yang, Yiming Ying, Ding-Xuan Zhou:
Generalization Analysis for Contrastive Representation Learning. CoRR abs/2302.12383 (2023) - [i43]Shaobo Lin, Di Wang, Ding-Xuan Zhou:
Sketching with Spherical Designs for Noisy Data Fitting on Spheres. CoRR abs/2303.04550 (2023) - [i42]Yunfei Yang, Ding-Xuan Zhou:
Optimal rates of approximation by shallow ReLUk neural networks and applications to nonparametric regression. CoRR abs/2304.01561 (2023) - [i41]Linhao Song, Jun Fan, Di-Rong Chen, Ding-Xuan Zhou:
Approximation of Nonlinear Functionals Using Deep ReLU Networks. CoRR abs/2304.04443 (2023) - [i40]Zhan Yu, Jun Fan, Ding-Xuan Zhou:
Distributed Gradient Descent for Functional Learning. CoRR abs/2305.07408 (2023) - [i39]Puyu Wang, Yunwen Lei, Di Wang, Yiming Ying, Ding-Xuan Zhou:
Generalization Guarantees of Gradient Descent for Multi-Layer Neural Networks. CoRR abs/2305.16891 (2023) - [i38]Junyu Zhou, Shuo Huang, Han Feng, Ding-Xuan Zhou:
Optimal Estimates for Pairwise Learning with Deep ReLU Networks. CoRR abs/2305.19640 (2023) - [i37]Yunfei Yang, Ding-Xuan Zhou:
Nonparametric regression using over-parameterized shallow ReLU neural networks. CoRR abs/2306.08321 (2023) - [i36]Zhongjie Shi, Zhan Yu, Ding-Xuan Zhou:
Learning Theory of Distribution Regression with Neural Networks. CoRR abs/2307.03487 (2023) - [i35]Tong Mao, Ding-Xuan Zhou:
Rates of Approximation by ReLU Shallow Neural Networks. CoRR abs/2307.12461 (2023) - [i34]Zhi Han, Baichen Liu, Shao-Bo Lin, Ding-Xuan Zhou:
Deep Convolutional Neural Networks with Zero-Padding: Feature Extraction and Learning. CoRR abs/2307.16203 (2023) - [i33]Zihan Zhang, Lei Shi, Ding-Xuan Zhou:
Classification with Deep Neural Networks and Logistic Loss. CoRR abs/2307.16792 (2023) - [i32]Guanhang Lei, Zhen Lei, Lei Shi, Chenyu Zeng, Ding-Xuan Zhou:
Solving PDEs on Spheres with Physics-Informed Convolutional Neural Networks. CoRR abs/2308.09605 (2023) - [i31]Di Wang, Xiaotong Liu, Shao-Bo Lin, Ding-Xuan Zhou:
Adaptive Distributed Kernel Ridge Regression: A Feasible Distributed Learning Scheme for Data Silos. CoRR abs/2309.04236 (2023) - [i30]Shao-Bo Lin, Tao Li, Shaojie Tang, Yao Wang, Ding-Xuan Zhou:
Lifting the Veil: Unlocking the Power of Depth in Q-learning. CoRR abs/2310.17915 (2023) - 2022
- [j70]Tong Mao, Ding-Xuan Zhou:
Approximation of functions from Korobov spaces by deep convolutional neural networks. Adv. Comput. Math. 48(6): 84 (2022) - [j69]Jinshan Zeng, Wotao Yin, Ding-Xuan Zhou:
Moreau Envelope Augmented Lagrangian Method for Nonconvex Optimization with Linear Constraints. J. Sci. Comput. 91(2): 61 (2022) - [j68]Han Feng, Sizai Hou, Le-Yin Wei, Ding-Xuan Zhou:
CNN models for readability of Chinese texts. Math. Found. Comput. 5(4): 351 (2022) - [j67]Zhi Han, Siquan Yu, Shao-Bo Lin, Ding-Xuan Zhou:
Depth Selection for Deep ReLU Nets in Feature Extraction and Generalization. IEEE Trans. Pattern Anal. Mach. Intell. 44(4): 1853-1868 (2022) - [j66]Shao-Bo Lin, Kaidong Wang, Yao Wang, Ding-Xuan Zhou:
Universal Consistency of Deep Convolutional Neural Networks. IEEE Trans. Inf. Theory 68(7): 4610-4617 (2022) - [j65]Charles K. Chui, Shao-Bo Lin, Bo Zhang, Ding-Xuan Zhou:
Realization of Spatial Sparseness by Deep ReLU Nets With Massive Data. IEEE Trans. Neural Networks Learn. Syst. 33(1): 229-243 (2022) - [c6]Jinshan Zeng, Yudong Xie, Xianglong Yu, John Lee, Ding-Xuan Zhou:
Enhancing Automatic Readability Assessment with Pre-training and Soft Labels for Ordinal Regression. EMNLP (Findings) 2022: 4557-4568 - [c5]Puyu Wang, Yunwen Lei, Yiming Ying, Ding-Xuan Zhou:
Stability and Generalization for Markov Chain Stochastic Gradient Methods. NeurIPS 2022 - [i29]Zhiying Fang, Yidong Ouyang, Ding-Xuan Zhou, Guang Cheng:
Attention Enables Zero Approximation Error. CoRR abs/2202.12166 (2022) - [i28]Puyu Wang, Yunwen Lei, Yiming Ying, Ding-Xuan Zhou:
Differentially Private Stochastic Gradient Descent with Low-Noise. CoRR abs/2209.04188 (2022) - [i27]Puyu Wang, Yunwen Lei, Yiming Ying, Ding-Xuan Zhou:
Stability and Generalization for Markov Chain Stochastic Gradient Methods. CoRR abs/2209.08005 (2022) - [i26]Han Feng, Jianfei Li, Ding-Xuan Zhou:
Approximation analysis of CNNs from feature extraction view. CoRR abs/2210.09041 (2022) - [i25]Jianfei Li, Han Feng, Ding-Xuan Zhou:
A new activation for neural networks and its approximation. CoRR abs/2210.10264 (2022) - 2021
- [j64]Ting Hu, Qiang Wu, Ding-Xuan Zhou:
Kernel gradient descent algorithm for information theoretic learning. J. Approx. Theory 263: 105518 (2021) - [j63]Jinshan Zeng, Shao-Bo Lin, Yuan Yao, Ding-Xuan Zhou:
On ADMM in Deep Learning: Convergence and Saturation-Avoidance. J. Mach. Learn. Res. 22: 199:1-199:67 (2021) - [j62]Tong Mao, Zhongjie Shi, Ding-Xuan Zhou:
Theory of deep convolutional neural networks III: Approximating radial functions. Neural Networks 144: 778-790 (2021) - [j61]Shaobo Lin, Yuguang Wang, Ding-Xuan Zhou:
Distributed Filtered Hyperinterpolation for Noisy Data on the Sphere. SIAM J. Numer. Anal. 59(2): 634-659 (2021) - [c4]Yuan Cao, Zhiying Fang, Yue Wu, Ding-Xuan Zhou, Quanquan Gu:
Towards Understanding the Spectral Bias of Deep Learning. IJCAI 2021: 2205-2211 - [i24]Zhan Yu, Daniel W. C. Ho, Ding-Xuan Zhou:
Robust Kernel-based Distribution Regression. CoRR abs/2104.10637 (2021) - [i23]Shao-Bo Lin, Kaidong Wang, Yao Wang, Ding-Xuan Zhou:
Universal Consistency of Deep Convolutional Neural Networks. CoRR abs/2106.12498 (2021) - [i22]Tong Mao, Zhongjie Shi, Ding-Xuan Zhou:
Theory of Deep Convolutional Neural Networks III: Approximating Radial Functions. CoRR abs/2107.00896 (2021) - [i21]Shao-Bo Lin, Yao Wang, Ding-Xuan Zhou:
Generalization Performance of Empirical Risk Minimization on Over-parameterized Deep ReLU Nets. CoRR abs/2111.14039 (2021) - [i20]Han Feng, Shao-Bo Lin, Ding-Xuan Zhou:
Radial Basis Function Approximation with Distributively Stored Data on Spheres. CoRR abs/2112.02499 (2021) - 2020
- [j60]Zhiying Fang, Zheng-Chu Guo, Ding-Xuan Zhou:
Optimal learning rates for distribution regression. J. Complex. 56 (2020) - [j59]Shao-Bo Lin, Di Wang, Ding-Xuan Zhou:
Distributed Kernel Ridge Regression with Communications. J. Mach. Learn. Res. 21: 93:1-93:38 (2020) - [j58]Ding-Xuan Zhou:
Theory of deep convolutional neural networks: Downsampling. Neural Networks 124: 319-327 (2020) - [j57]Zhiying Fang, Han Feng, Shuo Huang, Ding-Xuan Zhou:
Theory of deep convolutional neural networks II: Spherical analysis. Neural Networks 131: 154-162 (2020) - [i19]Shao-Bo Lin, Di Wang, Ding-Xuan Zhou:
Distributed Kernel Ridge Regression with Communications. CoRR abs/2003.12210 (2020) - [i18]Zhi Han, Siquan Yu, Shao-Bo Lin, Ding-Xuan Zhou:
Depth Selection for Deep ReLU Nets in Feature Extraction and Generalization. CoRR abs/2004.00245 (2020) - [i17]Zhiying Fang, Han Feng, Shuo Huang, Ding-Xuan Zhou:
Theory of Deep Convolutional Neural Networks II: Spherical Analysis. CoRR abs/2007.14285 (2020)
2010 – 2019
- 2019
- [j56]Charles K. Chui, Shao-Bo Lin, Ding-Xuan Zhou:
Deep Net Tree Structure for Balance of Capacity and Approximation Ability. Frontiers Appl. Math. Stat. 5: 46 (2019) - [j55]Shao-Bo Lin, Yunwen Lei, Ding-Xuan Zhou:
Boosted Kernel Ridge Regression: Optimal Learning Rates and Early Stopping. J. Mach. Learn. Res. 20: 46:1-46:36 (2019) - [j54]Yunwen Lei, Ürün Dogan, Ding-Xuan Zhou, Marius Kloft:
Data-Dependent Generalization Bounds for Multi-Class Classification. IEEE Trans. Inf. Theory 65(5): 2995-3021 (2019) - [c3]Yunwen Lei, Peng Yang, Ke Tang, Ding-Xuan Zhou:
Optimal Stochastic and Online Learning with Individual Iterates. NeurIPS 2019: 5416-5426 - [i16]Charles K. Chui, Shao-Bo Lin, Ding-Xuan Zhou:
Deep Neural Networks for Rotation-Invariance Approximation and Learning. CoRR abs/1904.01814 (2019) - [i15]Shao-Bo Lin, Yu Guang Wang, Ding-Xuan Zhou:
Distributed filtered hyperinterpolation for noisy data on the sphere. CoRR abs/1910.02434 (2019) - [i14]Jinshan Zeng, Minrun Wu, Shao-Bo Lin, Ding-Xuan Zhou:
Fast Polynomial Kernel Classification for Massive Data. CoRR abs/1911.10558 (2019) - [i13]Yuan Cao, Zhiying Fang, Yue Wu, Ding-Xuan Zhou, Quanquan Gu:
Towards Understanding the Spectral Bias of Deep Learning. CoRR abs/1912.01198 (2019) - [i12]Charles K. Chui, Shao-Bo Lin, Bo Zhang, Ding-Xuan Zhou:
Realization of spatial sparseness by deep ReLU nets with massive data. CoRR abs/1912.07464 (2019) - 2018
- [j53]Junhong Lin, Lorenzo Rosasco, Silvia Villa, Ding-Xuan Zhou:
Modified Fejér sequences and applications. Comput. Optim. Appl. 71(1): 95-113 (2018) - [j52]Charles K. Chui, Shao-Bo Lin, Ding-Xuan Zhou:
Construction of Neural Networks for Realization of Localized Deep Learning. Frontiers Appl. Math. Stat. 4: 14 (2018) - [j51]Andreas Christmann, Dao-Hong Xiang, Ding-Xuan Zhou:
Total stability of kernel methods. Neurocomputing 289: 101-118 (2018) - [j50]Yunwen Lei, Ding-Xuan Zhou:
Learning Theory of Randomized Sparse Kaczmarz Method. SIAM J. Imaging Sci. 11(1): 547-574 (2018) - [j49]Junhong Lin, Ding-Xuan Zhou:
Online Learning Algorithms Can Converge Comparably Fast as Batch Learning. IEEE Trans. Neural Networks Learn. Syst. 29(6): 2367-2378 (2018) - [i11]Yunwen Lei, Ding-Xuan Zhou:
Convergence of Online Mirror Descent Algorithms. CoRR abs/1802.06357 (2018) - [i10]Charles K. Chui, Shaobo Lin, Ding-Xuan Zhou:
Construction of neural networks for realization of localized deep learning. CoRR abs/1803.03503 (2018) - [i9]Ding-Xuan Zhou:
Universality of Deep Convolutional Neural Networks. CoRR abs/1805.10769 (2018) - 2017
- [j48]Zheng-Chu Guo, Yiming Ying, Ding-Xuan Zhou:
Online regularized learning with pairwise loss functions. Adv. Comput. Math. 43(1): 127-150 (2017) - [j47]Junhong Lin, Yunwen Lei, Bo Zhang, Ding-Xuan Zhou:
Online pairwise learning algorithms with convex loss functions. Inf. Sci. 406: 57-70 (2017) - [j46]Bing-Zheng Li, Bo-Lu He, Ding-Xuan Zhou:
Approximation on variable exponent spaces by linear integral operators. J. Approx. Theory 223: 29-51 (2017) - [j45]Xiangyu Chang, Shaobo Lin, Ding-Xuan Zhou:
Distributed Semi-supervised Learning with Kernel Ridge Regression. J. Mach. Learn. Res. 18: 46:1-46:22 (2017) - [j44]Shaobo Lin, Xin Guo, Ding-Xuan Zhou:
Distributed Learning with Regularized Least Squares. J. Mach. Learn. Res. 18: 92:1-92:31 (2017) - [j43]Yunwen Lei, Ding-Xuan Zhou:
Analysis of Online Composite Mirror Descent Algorithm. Neural Comput. 29(3): 825-860 (2017) - [i8]Yunwen Lei, Ürün Dogan, Ding-Xuan Zhou, Marius Kloft:
Generalization Error Bounds for Extreme Multi-class Classification. CoRR abs/1706.09814 (2017) - [i7]Andreas Christmann, Dao-Hong Xiang, Ding-Xuan Zhou:
Total stability of kernel methods. CoRR abs/1709.07625 (2017) - 2016
- [j42]Andreas Christmann, Ding-Xuan Zhou:
On the robustness of regularized pairwise learning methods based on kernels. J. Complex. 37: 1-33 (2016) - [j41]Junhong Lin, Lorenzo Rosasco, Ding-Xuan Zhou:
Iterative Regularization for Learning with Convex Loss Functions. J. Mach. Learn. Res. 17: 77:1-77:38 (2016) - [j40]Xin Guo, Jun Fan, Ding-Xuan Zhou:
Sparsity and Error Analysis of Empirical Feature-Based Regularization Schemes. J. Mach. Learn. Res. 17: 89:1-89:34 (2016) - [j39]Yiming Ying, Ding-Xuan Zhou:
Online Pairwise Learning Algorithms. Neural Comput. 28(4): 743-777 (2016) - [j38]Ting Hu, Qiang Wu, Ding-Xuan Zhou:
Convergence of Gradient Descent for Minimum Error Entropy Principle in Linear Regression. IEEE Trans. Signal Process. 64(24): 6571-6579 (2016) - [c2]Martin Boissier, Siwei Lyu, Yiming Ying, Ding-Xuan Zhou:
Fast Convergence of Online Pairwise Learning Algorithms. AISTATS 2016: 204-212 - [i6]Shaobo Lin, Xin Guo, Ding-Xuan Zhou:
Distributed Learning with Regularized Least Squares. CoRR abs/1608.03339 (2016) - 2015
- [j37]Junhong Lin, Ding-Xuan Zhou:
Learning theory of randomized Kaczmarz algorithm. J. Mach. Learn. Res. 16: 3341-3365 (2015) - [i5]Yiming Ying, Ding-Xuan Zhou:
Online Pairwise Learning Algorithms with Kernels. CoRR abs/1502.07229 (2015) - [i4]Yiming Ying, Ding-Xuan Zhou:
Unregularized Online Learning Algorithms with General Loss Functions. CoRR abs/1503.00623 (2015) - [i3]Ming Yuan, Ding-Xuan Zhou:
Minimax Optimal Rates of Estimation in High Dimensional Additive Models: Universal Phase Transition. CoRR abs/1503.02817 (2015) - 2014
- [j36]Anyue Chen, Junping Li, Yiqing Chen, Ding-Xuan Zhou:
Asymptotic Behaviour of Extinction Probability of Interacting Branching Collision Processes. J. Appl. Probab. 51(1): 219-234 (2014) - [i2]Jun Fan, Ting Hu, Qiang Wu, Ding-Xuan Zhou:
Consistency Analysis of an Empirical Minimum Error Entropy Algorithm. CoRR abs/1412.5272 (2014) - 2013
- [j35]Zheng-Chu Guo, Ding-Xuan Zhou:
Concentration estimates for learning with unbounded sampling. Adv. Comput. Math. 38(1): 207-223 (2013) - [j34]Hong-Yan Wang, Quan-Wu Xiao, Ding-Xuan Zhou:
An approximation theory approach to learning with ℓ1 regularization. J. Approx. Theory 167: 240-258 (2013) - [j33]Ting Hu, Jun Fan, Qiang Wu, Ding-Xuan Zhou:
Learning theory approach to minimum error entropy criterion. J. Mach. Learn. Res. 14(1): 377-397 (2013) - 2012
- [j32]Dao-Hong Xiang, Ting Hu, Ding-Xuan Zhou:
Approximation Analysis of Learning Algorithms for Support Vector Regression and Quantile Regression. J. Appl. Math. 2012: 902139:1-902139:17 (2012) - [i1]Ting Hu, Jun Fan, Qiang Wu, Ding-Xuan Zhou:
Learning Theory Approach to Minimum Error Entropy Criterion. CoRR abs/1208.0848 (2012) - 2011
- [j31]Dao-Hong Xiang, Ting Hu, Ding-Xuan Zhou:
Learning with varying insensitive loss. Appl. Math. Lett. 24(12): 2107-2109 (2011) - [j30]Cheng Wang, Ding-Xuan Zhou:
Optimal learning rates for least squares regularized regression with unbounded sampling. J. Complex. 27(1): 55-67 (2011) - [j29]Lei Shi, Ding-Xuan Zhou:
Normal estimation on manifolds by gradient learning. Numer. Linear Algebra Appl. 18(2): 249-259 (2011) - 2010
- [j28]Hong-Yan Wang, Dao-Hong Xiang, Ding-Xuan Zhou:
Moving least-square method in learning theory. J. Approx. Theory 162(3): 599-614 (2010) - [j27]Lei Shi, Xin Guo, Ding-Xuan Zhou:
Hermite learning with gradient data. J. Comput. Appl. Math. 233(11): 3046-3059 (2010)
2000 – 2009
- 2009
- [j26]Xiang-Jun Zhou, Ding-Xuan Zhou:
High order Parzen windows and randomized sampling. Adv. Comput. Math. 31(4): 349-368 (2009) - [j25]Jia Cai, Hongyan Wang, Ding-Xuan Zhou:
Gradient learning in a classification setting by gradient descent. J. Approx. Theory 161(2): 674-692 (2009) - [j24]Dao-Hong Xiang, Ding-Xuan Zhou:
Classification with Gaussians and Convex Loss. J. Mach. Learn. Res. 10: 1447-1468 (2009) - [j23]Ting Hu, Ding-Xuan Zhou:
Online Learning with Samples Drawn from Non-identical Distributions. J. Mach. Learn. Res. 10: 2873-2898 (2009) - 2008
- [j22]Gui-Bo Ye, Ding-Xuan Zhou:
Learning and approximation by Gaussians on Riemannian manifolds. Adv. Comput. Math. 29(3): 291-310 (2008) - [j21]Qiang Wu, Ding-Xuan Zhou:
Learning with sample dependent hypothesis spaces. Comput. Math. Appl. 56(11): 2896-2907 (2008) - [j20]Zhi-Wei Pan, Dao-Hong Xiang, Quan-Wu Xiao, Ding-Xuan Zhou:
Parzen windows for multi-class classification. J. Complex. 24(5-6): 606-618 (2008) - 2007
- [j19]Qiang Wu, Yiming Ying, Ding-Xuan Zhou:
Multi-kernel regularized classifiers. J. Complex. 23(1): 108-134 (2007) - [j18]Yiming Ying, Ding-Xuan Zhou:
Learnability of Gaussians with Flexible Variances. J. Mach. Learn. Res. 8: 249-276 (2007) - 2006
- [j17]Ding-Xuan Zhou, Kurt Jetter:
Approximation with polynomial kernels and SVM classifiers. Adv. Comput. Math. 25(1-3): 323-344 (2006) - [j16]Qiang Wu, Yiming Ying, Ding-Xuan Zhou:
Learning Rates of Least-Square Regularized Regression. Found. Comput. Math. 6(2): 171-192 (2006) - [j15]Sayan Mukherjee, Ding-Xuan Zhou:
Learning Coordinate Covariances via Gradients. J. Mach. Learn. Res. 7: 519-549 (2006) - [j14]Yiming Ying, Ding-Xuan Zhou:
Online Regularized Classification Algorithms. IEEE Trans. Inf. Theory 52(11): 4775-4788 (2006) - 2005
- [j13]Qiang Wu, Ding-Xuan Zhou:
SVM Soft Margin Classifiers: Linear Programming versus Quadratic Programming. Neural Comput. 17(5): 1160-1187 (2005) - 2004
- [j12]Felipe Cucker, Steve Smale, Ding-Xuan Zhou:
Modeling Language Evolution. Found. Comput. Math. 4(3): 315-343 (2004) - [j11]Di-Rong Chen, Qiang Wu, Yiming Ying, Ding-Xuan Zhou:
Support Vector Machine Soft Margin Classifiers: Error Analysis. J. Mach. Learn. Res. 5: 1143-1175 (2004) - 2003
- [j10]Gerlind Plonka, Ding-Xuan Zhou:
Properties of locally linearly independent refinable function vectors. J. Approx. Theory 122(1): 24-41 (2003) - [j9]Ding-Xuan Zhou:
Capacity of reproducing kernel spaces in learning theory. IEEE Trans. Inf. Theory 49(7): 1743-1752 (2003) - 2002
- [j8]Hoi Ling Cheung, Canqin Tang, Ding-Xuan Zhou:
Supports of Locally Linearly Independent M-Refinable Functions, Attractors of Iterated Function Systems and Tilings. Adv. Comput. Math. 17(3): 257-268 (2002) - [j7]Ding-Xuan Zhou:
The covering number in learning theory. J. Complex. 18(3): 739-767 (2002) - [j6]Ding-Xuan Zhou:
Interpolatory orthogonal multiwavelets and refinable functions. IEEE Trans. Signal Process. 50(3): 520-527 (2002) - 2001
- [j5]Geoff Boyd, Charles A. Micchelli, Gilbert Strang, Ding-Xuan Zhou:
Binomial Matrices. Adv. Comput. Math. 14(4): 379-391 (2001) - [j4]Ding-Xuan Zhou:
Self-Similar Lattice Tilings and Subdivision Schemes. SIAM J. Math. Anal. 33(1): 1-15 (2001) - 2000
- [j3]Rong-Qing Jia, Ding-Xuan Zhou:
Convergence of Subdivision Schemes Associated with Nonnegative Masks. SIAM J. Matrix Anal. Appl. 21(2): 418-430 (2000)
1990 – 1999
- 1999
- [j2]Rong-Qing Jia, Sherman D. Riemenschneider, Ding-Xuan Zhou:
Smoothness of Multiple Refinable Functions and Multiple Wavelets. SIAM J. Matrix Anal. Appl. 21(1): 1-28 (1999) - [c1]Daniel W. C. Ho, Jinhua Xu, Ding-Xuan Zhou:
System identification using wavelet neural networks. ECC 1999: 2245-2250 - 1998
- [j1]Rong-Qing Jia, Sherman D. Riemenschneider, Ding-Xuan Zhou:
Vector subdivision schemes and multiple wavelets. Math. Comput. 67(224): 1533-1563 (1998)
Coauthor Index
Shaobo Lin
aka: Shao-Bo Lin
aka: Shao-Bo Lin
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last updated on 2024-11-06 21:33 CET by the dblp team
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