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1. Full Text Universality of deep convolutional neural networks
by Zhou, Ding-Xuan
Applied and computational harmonic analysis, ISSN 1063-5203, 03/2020, Volume 48, Issue 2, pp. 787 - 794
Deep learning | Approximation theory | Universality | Convolutional neural network | Physical Sciences | Mathematics | Mathematics, Applied | Science & Technology
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2. Deep distributed convolutional neural networks: Universality
by Zhou, Ding-Xuan
Analysis and applications, ISSN 0219-5305, 11/2018, Volume 16, Issue 6, pp. 895 - 919
Deep learning | universality | deep distributed convolutional neural networks | convolutional neural networks | filter mask | Physical Sciences | Mathematics | Mathematics, Applied | Science & Technology
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3. Full Text Iterative regularization for learning with convex loss functions
by Lin, Junhong and Rosasco, Lorenzo and Zhou, Ding-Xuan
Journal of machine learning research, ISSN 1532-4435, 05/2016, Volume 17
Automation & Control Systems | Computer Science, Artificial Intelligence | Technology | Computer Science | Science & Technology
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4. Full Text Online pairwise learning algorithms
by Ying, Yiming and Zhou, Ding-Xuan
Neural computation, ISSN 0899-7667, 04/2016, Volume 28, Issue 4, pp. 743 - 777
Neurosciences | Life Sciences & Biomedicine | Technology | Computer Science | Computer Science, Artificial Intelligence | Neurosciences & Neurology | Science & Technology | Learning | Probability | Algorithms | Random variables | Convergence | Kernels | Distance learning | Mathematical analysis | Hilbert space | Mathematical models
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5. Full Text Unregularized online learning algorithms with general loss functions
by Ying, Yiming and Zhou, Ding-Xuan
Applied and computational harmonic analysis, ISSN 1063-5203, 03/2017, Volume 42, Issue 2, pp. 224 - 244
Pairwise learning | Reproducing kernel Hilbert space | Bipartite ranking | Learning theory | Online learning | Physical Sciences | Mathematics | Mathematics, Applied | Science & Technology | Online education | Algorithms | Data mining | Analysis
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6. Full Text Distributed learning with regularized least squares
by Lin, Shao-Bo and Guo, Xin and Zhou, Ding-Xuan
Journal of machine learning research, ISSN 1532-4435, 09/2017, Volume 18, pp. 1 - 31
Integral operator second order decomposition | Distributed learning | Error analysis | Divide-and-conquer | Automation & Control Systems | Computer Science, Artificial Intelligence | Technology | Computer Science | Science & Technology
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7. Full Text Convergence of online mirror descent
by Lei, Yunwen and Zhou, Ding-Xuan
Applied and computational harmonic analysis, ISSN 1063-5203, 01/2020, Volume 48, Issue 1, pp. 343 - 373
Mirror descent | Learning theory | Bregman distance | Online learning | Convergence analysis | Physical Sciences | Mathematics | Mathematics, Applied | Science & Technology | Data mining | Analysis | Algorithms
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8. Full Text Learning Theory Estimates via Integral Operators and Their Approximations
by Smale, Steve and Zhou, Ding-Xuan
Constructive approximation, ISSN 0176-4276, 8/2007, Volume 26, Issue 2, pp. 153 - 172
Numerical Analysis | Analysis | Mathematics | Regularization scheme | Reproducing kernel Hilbert space | Learning theory | Sampling operator | Vector-valued random variable | Physical Sciences | Science & Technology
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9. Full Text Learning theory of randomized Kaczmarz algorithm
by Lin, Junhong and Zhou, Ding-Xuan
Journal of machine learning research, ISSN 1532-4435, 12/2015, Volume 16, pp. 3341 - 3365
Relaxed randomized Kaczmarz algorithm | Almost sure convergence | Learning theory | Online learning | Space of homogeneous linear functions | Automation & Control Systems | Computer Science, Artificial Intelligence | Technology | Computer Science | Science & Technology
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10. Full Text MINIMAX OPTIMAL RATES OF ESTIMATION IN HIGH DIMENSIONAL ADDITIVE MODELS
by Ming Yuan and Ding-Xuan Zhou
The Annals of statistics, ISSN 0090-5364, 12/2016, Volume 44, Issue 6, pp. 2564 - 2593
Minimax | Linear regression | Machine learning | Eigenvalues | Component functions | Eigenfunctions | Hilbert spaces | Nonparametric models | Linear models | Estimators | Convergence rate | Reproducing kernel Hilbert space | Method of regularization | Sobolev space | Minimax optimality | Statistics & Probability | Physical Sciences | Mathematics | Science & Technology | Studies | Mathematical problems | Mathematical models | Mathematical functions | Convergence
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11. Full Text Consistency analysis of an empirical minimum error entropy algorithm
by Fan, Jun and Hu, Ting and Wu, Qiang and Zhou, Ding-Xuan
Applied and computational harmonic analysis, ISSN 1063-5203, 07/2016, Volume 41, Issue 1, pp. 164 - 189
Rényi's entropy | Error entropy consistency | Learning theory | Regression consistency | Minimum error entropy | Analysis | Algorithms | Errors | Mathematical analysis | Consistency | Bandwidth | Regression | Mathematical models | Entropy
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12. Full Text Data-Dependent Generalization Bounds for Multi-Class Classification
by Lei, Yunwen and Dogan, Urun and Zhou, Ding-Xuan and Kloft, Marius
IEEE transactions on information theory, ISSN 0018-9448, 05/2019, Volume 65, Issue 5, pp. 2995 - 3021
Training | Support vector machines | Computer science | Upper bound | Gaussian complexities | generalization error bounds | covering numbers | Gaussian processes | Multi-class classification | Complexity theory | Rademacher complexities | Standards | Computer Science, Information Systems | Engineering, Electrical & Electronic | Engineering | Technology | Computer Science | Science & Technology | Learning | Algorithms | Error analysis | Upper bounds | Dependence | Empirical analysis | Continuity (mathematics) | Linear functions
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13. Full Text Sparsity and error analysis of empirical feature-based regularization schemes
by Guo, Xin and Fan, Jun and Zhou, Ding-Xuan
Journal of machine learning research, ISSN 1532-4435, 06/2016, Volume 17, pp. 1 - 34
Reproducing kernel Hilbert space | Regularization with empirical features | Sparsity | Concave regularizer | penalty | SCAD penalty | Automation & Control Systems | Computer Science, Artificial Intelligence | Technology | Computer Science | Science & Technology
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14. Full Text Distributed semi-supervised learning with kernel ridge regression
by Chang, Xiangyu and Lin, Shao-Bo and Zhou, Ding-Xuan
Journal of machine learning research, ISSN 1532-4435, 05/2017, Volume 18, pp. 1 - 22
Error decomposition | Unlabeled data | Kernel ridge regression | Distributed learning | Learning theory | Semi-supervised learning | Automation & Control Systems | Computer Science, Artificial Intelligence | Technology | Computer Science | Science & Technology
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15. Full Text Diffused Phase Transition Boosts Thermal Stability of High‐Performance Lead‐Free Piezoelectrics
by Yao, Fang‐Zhou and Wang, Ke and Jo, Wook and Webber, Kyle G and Comyn, Timothy P and Ding, Jing‐Xuan and Xu, Ben and Cheng, Li‐Qian and Zheng, Mu‐Peng and Hou, Yu‐Dong and Li, Jing‐Feng
Advanced functional materials, ISSN 1616-301X, 02/2016, Volume 26, Issue 8, pp. 1217 - 1224
niobate | lead‐free | phase transition | temperature stability | piezoelectric | lead-free | Materials Science | Materials Science, Multidisciplinary | Physics, Condensed Matter | Science & Technology - Other Topics | Chemistry, Physical | Physics | Science & Technology | Physics, Applied | Physical Sciences | Chemistry | Nanoscience & Nanotechnology | Technology | Chemistry, Multidisciplinary | Stability | Phase transformations | Piezoelectricity | Constants | Lead free | Diffusion | Thermal stability | Strain
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16. Full Text Derivative reproducing properties for kernel methods in learning theory
by Zhou, Ding-Xuan
Journal of computational and applied mathematics, ISSN 0377-0427, 10/2008, Volume 220, Issue 1-2, pp. 456 - 463
Representer theorem | Reproducing kernel Hilbert spaces | Hermite learning and semi-supervised learning | Learning theory | Derivative reproducing | Physical Sciences | Mathematics | Mathematics, Applied | Science & Technology | Numerical analysis. Scientific computation | Numerical analysis | Mathematical analysis | Exact sciences and technology | Functional analysis | Sciences and techniques of general use | Analysis | Methods | Algorithms
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17. Full Text Total stability of kernel methods
by Christmann, Andreas and Xiang, Daohong and Zhou, Ding-Xuan
Neurocomputing (Amsterdam), ISSN 0925-2312, 05/2018, Volume 289, pp. 101 - 118
Robustness | Stability | Kernel | Regularization | Machine learning | Computer Science, Artificial Intelligence | Technology | Computer Science | Science & Technology
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