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ORCIDJohan A. K. Suykens
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- affiliation: KU Leuven, Department of Electrical Engineering, Belgium
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2020 – today
- 2023
[i60]Arun Pandey, Hannes De Meulemeester, Bart De Moor, Johan A. K. Suykens:
Multi-view Kernel PCA for Time series Forecasting. CoRR abs/2301.09811 (2023)- 2022
- [j187]Michaël Fanuel
, Joachim Schreurs, Johan A. K. Suykens:
Nyström landmark sampling and regularized Christoffel functions. Mach. Learn. 111(6): 2213-2254 (2022) - [j186]Arun Pandey, Michaël Fanuel, Joachim Schreurs, Johan A. K. Suykens:
Disentangled Representation Learning and Generation With Manifold Optimization. Neural Comput. 34(10): 2009-2036 (2022) - [j185]Fanghui Liu, Xiaolin Huang, Yudong Chen, Johan A. K. Suykens:
Random Features for Kernel Approximation: A Survey on Algorithms, Theory, and Beyond. IEEE Trans. Pattern Anal. Mach. Intell. 44(10): 7128-7148 (2022) - [j184]Fanghui Liu, Xiaolin Huang, Yudong Chen, Johan A. K. Suykens:
Towards a Unified Quadrature Framework for Large-Scale Kernel Machines. IEEE Trans. Pattern Anal. Mach. Intell. 44(11): 7975-7988 (2022) - [j183]Michaël Fanuel, Antoine Aspeel, Jean-Charles Delvenne, Johan A. K. Suykens:
Positive Semi-definite Embedding for Dimensionality Reduction and Out-of-Sample Extensions. SIAM J. Math. Data Sci. 4(1): 153-178 (2022) - [j182]Qinghua Tao, Zhen Li, Jun Xu, Shu Lin, Bart De Schutter, Johan A. K. Suykens:
Short-Term Traffic Flow Prediction Based on the Efficient Hinging Hyperplanes Neural Network. IEEE Trans. Intell. Transp. Syst. 23(9): 15616-15628 (2022) - [j181]Qinghua Tao, Jun Xu, Zhen Li, Na Xie, Shuning Wang, Xiaoli Li, Johan A. K. Suykens:
Toward Deep Adaptive Hinging Hyperplanes. IEEE Trans. Neural Networks Learn. Syst. 33(11): 6373-6387 (2022) - [i59]Mingzhen He, Fan He, Lei Shi, Xiaolin Huang, Johan A. K. Suykens:
Learning with Asymmetric Kernels: Least Squares and Feature Interpretation. CoRR abs/2202.01397 (2022) - [i58]Qinghua Tao, Li Li, Xiaolin Huang, Xiangming Xi, Shuning Wang, Johan A. K. Suykens:
Piecewise Linear Neural Networks and Deep Learning. CoRR abs/2206.09149 (2022) - [i57]Yingyi Chen, Shell Xu Hu, Xi Shen, Chunrong Ai, Johan A. K. Suykens:
Compressing Features for Learning with Noisy Labels. CoRR abs/2206.13140 (2022) - [i56]Qinghua Tao, Francesco Tonin, Panagiotis Patrinos, Johan A. K. Suykens:
Tensor-based Multi-view Spectral Clustering via Shared Latent Space. CoRR abs/2207.11559 (2022) - [i55]Yingyi Chen, Xi Shen, Yahui Liu, Qinghua Tao, Johan A. K. Suykens:
Jigsaw-ViT: Learning Jigsaw Puzzles in Vision Transformer. CoRR abs/2207.11971 (2022) - 2021
- [j180]Qinghua Tao, Zhen Li, Jun Xu, Na Xie, Shuning Wang, Johan A. K. Suykens:
Learning with continuous piecewise linear decision trees. Expert Syst. Appl. 168: 114214 (2021) - [j179]Xin Ma, Mei Xie, Johan A. K. Suykens:
A novel neural grey system model with Bayesian regularization and its applications. Neurocomputing 456: 61-75 (2021) - [j178]Lynn Houthuys, Johan A. K. Suykens:
Tensor-based restricted kernel machines for multi-view classification. Inf. Fusion 68: 54-66 (2021) - [j177]Kevin Villalobos, Johan A. K. Suykens, Arantza Illarramendi:
A flexible alarm prediction system for smart manufacturing scenarios following a forecaster-analyzer approach. J. Intell. Manuf. 32(5): 1323-1344 (2021) - [j176]Fanghui Liu, Lei Shi, Xiaolin Huang, Jie Yang, Johan A. K. Suykens:
Generalization Properties of hyper-RKHS and its Applications. J. Mach. Learn. Res. 22: 140:1-140:38 (2021) - [j175]Fanghui Liu, Lei Shi, Xiaolin Huang, Jie Yang, Johan A. K. Suykens:
Analysis of regularized least-squares in reproducing kernel Kreĭn spaces. Mach. Learn. 110(6): 1145-1173 (2021) - [j174]Arun Pandey, Joachim Schreurs, Johan A. K. Suykens:
Generative Restricted Kernel Machines: A framework for multi-view generation and disentangled feature learning. Neural Networks 135: 177-191 (2021) - [j173]Francesco Tonin, Panagiotis Patrinos, Johan A. K. Suykens:
Unsupervised learning of disentangled representations in deep restricted kernel machines with orthogonality constraints. Neural Networks 142: 661-679 (2021) - [j172]Joachim Schreurs, Iwein Vranckx, Mia Hubert, Johan A. K. Suykens, Peter J. Rousseeuw:
Outlier detection in non-elliptical data by kernel MRCD. Stat. Comput. 31(5): 66 (2021) - [j171]Michaël Fanuel, Joachim Schreurs, Johan A. K. Suykens:
Diversity Sampling is an Implicit Regularization for Kernel Methods. SIAM J. Math. Data Sci. 3(1): 280-297 (2021) - [c180]Fanghui Liu, Xiaolin Huang, Yingyi Chen, Johan A. K. Suykens:
Fast Learning in Reproducing Kernel Krein Spaces via Signed Measures. AISTATS 2021: 388-396 - [c179]Fanghui Liu, Zhenyu Liao, Johan A. K. Suykens:
Kernel regression in high dimensions: Refined analysis beyond double descent. AISTATS 2021: 649-657 - [c178]Brecht Evens, Puya Latafat, Andreas Themelis, Johan A. K. Suykens, Panagiotis Patrinos:
Neural Network Training as an Optimal Control Problem : - An Augmented Lagrangian Approach -. CDC 2021: 5136-5143 - [c177]Yingyi Chen, Xi Shen, Shell Xu Hu, Johan A. K. Suykens:
Boosting Co-Teaching With Compression Regularization for Label Noise. CVPR Workshops 2021: 2688-2692 - [c176]Francesco Tonin, Arun Pandey, Panagiotis Patrinos, Johan A. K. Suykens:
Unsupervised Energy-based Out-of-distribution Detection using Stiefel-Restricted Kernel Machine. IJCNN 2021: 1-8 - [c175]Marcin Orchel, Johan A. K. Suykens:
Improved Update Rule and Sampling of Stochastic Gradient Descent with Extreme Early Stopping for Support Vector Machines. LOD 2021: 147-161 - [c174]Joachim Schreurs, Hannes De Meulemeester, Michaël Fanuel, Bart De Moor, Johan A. K. Suykens:
Leverage Score Sampling for Complete Mode Coverage in Generative Adversarial Networks. LOD 2021: 466-480 - [c173]Hannes De Meulemeester, Joachim Schreurs, Michaël Fanuel, Bart De Moor, Johan A. K. Suykens:
The Bures Metric for Generative Adversarial Networks. ECML/PKDD (2) 2021: 52-66 - [c172]Johan A. K. Suykens:
Kernel Machines in Time (Invited Talk). TIME 2021: 3:1-3:1 - [i54]Francesco Tonin, Arun Pandey, Panagiotis Patrinos, Johan A. K. Suykens:
Unsupervised Energy-based Out-of-distribution Detection using Stiefel-Restricted Kernel Machine. CoRR abs/2102.08443 (2021) - [i53]Joachim Schreurs, Hannes De Meulemeester, Michaël Fanuel, Bart De Moor, Johan A. K. Suykens:
Leverage Score Sampling for Complete Mode Coverage in Generative Adversarial Networks. CoRR abs/2104.02373 (2021) - [i52]Yingyi Chen, Xi Shen, Shell Xu Hu, Johan A. K. Suykens:
Boosting Co-teaching with Compression Regularization for Label Noise. CoRR abs/2104.13766 (2021) - [i51]Joachim Schreurs, Michaël Fanuel, Johan A. K. Suykens:
Towards Deterministic Diverse Subset Sampling. CoRR abs/2105.13942 (2021) - [i50]David Winant, Joachim Schreurs, Johan A. K. Suykens:
Latent Space Exploration Using Generative Kernel PCA. CoRR abs/2105.13949 (2021) - [i49]Fanghui Liu, Johan A. K. Suykens, Volkan Cevher:
On the Double Descent of Random Features Models Trained with SGD. CoRR abs/2110.06910 (2021) - [i48]Maximilian Lucassen, Johan A. K. Suykens, Kim Batselier:
Tensor Network Kalman Filtering for Large-Scale LS-SVMs. CoRR abs/2110.13501 (2021) - 2020
- [j170]Jun Xu, Qinghua Tao, Zhen Li, Xiangming Xi, Johan A. K. Suykens, Shuning Wang:
Efficient hinging hyperplanes neural network and its application in nonlinear system identification. Autom. 116: 108906 (2020) - [j169]Yunlong Feng, Jun Fan, Johan A. K. Suykens:
A Statistical Learning Approach to Modal Regression. J. Mach. Learn. Res. 21: 2:1-2:35 (2020) - [j168]Zahra Karevan, Johan A. K. Suykens:
Transductive LSTM for time-series prediction: An application to weather forecasting. Neural Networks 125: 1-9 (2020) - [j167]Fanghui Liu, Xiaolin Huang, Lei Shi, Jie Yang, Johan A. K. Suykens:
A Double-Variational Bayesian Framework in Random Fourier Features for Indefinite Kernels. IEEE Trans. Neural Networks Learn. Syst. 31(8): 2965-2979 (2020) - [c171]Fanghui Liu, Xiaolin Huang, Yudong Chen, Jie Yang, Johan A. K. Suykens:
Random Fourier Features via Fast Surrogate Leverage Weighted Sampling. AAAI 2020: 4844-4851 - [c170]Siamak Mehrkanoon, Xiaolin Huang, Johan A. K. Suykens:
Learning from partially labeled data. ESANN 2020: 493-502 - [c169]Henri De Plaen, Michaël Fanuel, Johan A. K. Suykens:
Wasserstein Exponential Kernels. IJCNN 2020: 1-6 - [c168]Marcin Orchel, Johan A. K. Suykens:
Fast Hyperparameter Tuning for Support Vector Machines with Stochastic Gradient Descent. LOD (2) 2020: 481-493 - [c167]Arun Pandey, Joachim Schreurs, Johan A. K. Suykens:
Robust Generative Restricted Kernel Machines Using Weighted Conjugate Feature Duality. LOD (1) 2020: 613-624 - [c166]Alexander Meulemans, Francesco S. Carzaniga, Johan A. K. Suykens, João Sacramento, Benjamin F. Grewe:
A Theoretical Framework for Target Propagation. NeurIPS 2020 - [i47]Arun Pandey, Joachim Schreurs, Johan A. K. Suykens:
Robust Generative Restricted Kernel Machines using Weighted Conjugate Feature Duality. CoRR abs/2002.01180 (2020) - [i46]Henri De Plaen, Michaël Fanuel, Johan A. K. Suykens:
Wasserstein Exponential Kernels. CoRR abs/2002.01878 (2020) - [i45]Michaël Fanuel, Joachim Schreurs, Johan A. K. Suykens:
Diversity sampling is an implicit regularization for kernel methods. CoRR abs/2002.08616 (2020) - [i44]Fanghui Liu, Xiaolin Huang, Yudong Chen, Johan A. K. Suykens:
Random Features for Kernel Approximation: A Survey in Algorithms, Theory, and Beyond. CoRR abs/2004.11154 (2020) - [i43]Fanghui Liu, Xiaolin Huang, Yingyi Chen, Johan A. K. Suykens:
Generalizing Random Fourier Features via Generalized Measures. CoRR abs/2006.00247 (2020) - [i42]Fanghui Liu, Lei Shi, Xiaolin Huang, Jie Yang, Johan A. K. Suykens:
Analysis of Least Squares Regularized Regression in Reproducing Kernel Krein Spaces. CoRR abs/2006.01073 (2020) - [i41]Arun Pandey, Michaël Fanuel, Joachim Schreurs, Johan A. K. Suykens:
Disentangled Representation Learning and Generation with Manifold Optimization. CoRR abs/2006.07046 (2020) - [i40]Hannes De Meulemeester, Joachim Schreurs, Michaël Fanuel, Bart De Moor, Johan A. K. Suykens:
The Bures Metric for Taming Mode Collapse in Generative Adversarial Networks. CoRR abs/2006.09096 (2020) - [i39]Joachim Schreurs, Michaël Fanuel, Johan A. K. Suykens:
Ensemble Kernel Methods, Implicit Regularization and Determinental Point Processes. CoRR abs/2006.13701 (2020) - [i38]Alexander Meulemans, Francesco S. Carzaniga, Johan A. K. Suykens, João Sacramento, Benjamin F. Grewe:
A Theoretical Framework for Target Propagation. CoRR abs/2006.14331 (2020) - [i37]Joachim Schreurs, Iwein Vranckx, Bart De Ketelaere, Mia Hubert, Johan A. K. Suykens, Peter J. Rousseeuw:
Outlier detection in non-elliptical data by kernel MRCD. CoRR abs/2008.02046 (2020) - [i36]Fanghui Liu, Zhenyu Liao, Johan A. K. Suykens:
Kernel regression in high dimension: Refined analysis beyond double descent. CoRR abs/2010.02681 (2020) - [i35]Fanghui Liu, Xiaolin Huang, Yudong Chen, Johan A. K. Suykens:
Towards a Unified Quadrature Framework for Large-Scale Kernel Machines. CoRR abs/2011.01668 (2020) - [i34]Michaël Fanuel, Joachim Schreurs, Johan A. K. Suykens:
Determinantal Point Processes Implicitly Regularize Semi-parametric Regression Problems. CoRR abs/2011.06964 (2020) - [i33]Francesco Tonin, Panagiotis Patrinos, Johan A. K. Suykens:
Unsupervised learning of disentangled representations in deep restricted kernel machines with orthogonality constraints. CoRR abs/2011.12659 (2020)
2010 – 2019
- 2019
- [j166]Sundaravelpandian Singaravel, Johan A. K. Suykens, Philipp Geyer:
Deep convolutional learning for general early design stage prediction models. Adv. Eng. Informatics 42 (2019) - [j165]Carlos M. Alaíz, Michaël Fanuel, Johan A. K. Suykens:
Robust classification of graph-based data. Data Min. Knowl. Discov. 33(1): 230-251 (2019) - [j164]Ricardo Castro-Garcia, Oscar Mauricio Agudelo, Johan A. K. Suykens:
Impulse response constrained LS-SVM modelling for MIMO Hammerstein system identification. Int. J. Control 92(4): 908-925 (2019) - [j163]Lei Shi, Xiaolin Huang, Yunlong Feng, Johan A. K. Suykens:
Sparse Kernel Regression with Coefficient-based $\ell_q-$regularization. J. Mach. Learn. Res. 20: 161:1-161:44 (2019) - [j162]Fanghui Liu, Xiaolin Huang, Chen Gong, Jie Yang, Johan A. K. Suykens:
Indefinite Kernel Logistic Regression With Concave-Inexact-Convex Procedure. IEEE Trans. Neural Networks Learn. Syst. 30(3): 765-776 (2019) - [c165]Joachim Schreurs, Michaël Fanuel, Johan A. K. Suykens:
Towards Deterministic Diverse Subset Sampling. BNAIC/BENELEARN 2019 - [c164]Joachim Schreurs, Michaël Fanuel, Johan A. K. Suykens:
Towards Deterministic Diverse Subset Sampling. BNAIC/BENELEARN (Selected Papers) 2019: 137-151 - [c163]David Winant, Joachim Schreurs, Johan A. K. Suykens:
Latent Space Exploration Using Generative Kernel PCA. BNAIC/BENELEARN 2019 - [c162]David Winant, Joachim Schreurs, Johan A. K. Suykens:
Latent Space Exploration Using Generative Kernel PCA. BNAIC/BENELEARN (Selected Papers) 2019: 70-82 - [c161]Marcin Orchel, Johan A. K. Suykens:
Axiomatic Kernels on Graphs for Support Vector Machines. ICANN (Workshop) 2019: 685-700 - [i32]Hanyuan Hang, Yingyi Chen, Johan A. K. Suykens:
Two-stage Best-scored Random Forest for Large-scale Regression. CoRR abs/1905.03438 (2019) - [i31]Jun Xu, Qinghua Tao, Zhen Li, Xiangming Xi, Johan A. K. Suykens, Shuning Wang:
Efficient hinging hyperplanes neural network and its application in nonlinear system identification. CoRR abs/1905.06518 (2019) - [i30]Michaël Fanuel, Joachim Schreurs, Johan A. K. Suykens:
Nyström landmark sampling and regularized Christoffel functions. CoRR abs/1905.12346 (2019) - [i29]Arun Pandey, Joachim Schreurs, Johan A. K. Suykens:
Generative Restricted Kernel Machines. CoRR abs/1906.08144 (2019) - [i28]Fanghui Liu, Xiaolin Huang, Yudong Chen, Jie Yang, Johan A. K. Suykens:
Random Fourier Features via Fast Surrogate Leverage Weighted Sampling. CoRR abs/1911.09158 (2019) - 2018
- [j161]Sundaravelpandian Singaravel, Johan A. K. Suykens, Philipp Geyer:
Deep-learning neural-network architectures and methods: Using component-based models in building-design energy prediction. Adv. Eng. Informatics 38: 81-90 (2018) - [j160]Giulio Bottegal, Ricardo Castro-Garcia, Johan A. K. Suykens:
A two-experiment approach to Wiener system identification. Autom. 93: 282-289 (2018) - [j159]Yuning Yang, Yunlong Feng, Johan A. K. Suykens:
Correntropy Based Matrix Completion. Entropy 20(3): 171 (2018) - [j158]Zahra Karevan, Johan A. K. Suykens:
Transductive Feature Selection Using Clustering-Based Sample Entropy for Temperature Prediction in Weather Forecasting. Entropy 20(4): 264 (2018) - [j157]Ricardo Castro-Garcia, Koen Tiels, Oscar Mauricio Agudelo, Johan A. K. Suykens:
Hammerstein system identification through best linear approximation inversion and regularisation. Int. J. Control 91(8): 1757-1773 (2018) - [j156]Lynn Houthuys, Rocco Langone, Johan A. K. Suykens:
Multi-View Least Squares Support Vector Machines Classification. Neurocomputing 282: 78-88 (2018) - [j155]Siamak Mehrkanoon, Johan A. K. Suykens:
Deep hybrid neural-kernel networks using random Fourier features. Neurocomputing 298: 46-54 (2018) - [j154]Xiaolin Huang, Lei Shi, Ming Yan, Johan A. K. Suykens:
Pinball loss minimization for one-bit compressive sensing: Convex models and algorithms. Neurocomputing 314: 275-283 (2018) - [j153]Carlos M. Alaíz, Johan A. K. Suykens:
Modified Frank-Wolfe algorithm for enhanced sparsity in support vector machine classifiers. Neurocomputing 320: 47-59 (2018) - [j152]Lynn Houthuys, Rocco Langone, Johan A. K. Suykens:
Multi-View Kernel Spectral Clustering. Inf. Fusion 44: 46-56 (2018) - [j151]Hanyuan Hang, Ingo Steinwart, Yunlong Feng, Johan A. K. Suykens:
Kernel Density Estimation for Dynamical Systems. J. Mach. Learn. Res. 19: 35:1-35:49 (2018) - [j150]Siamak Mehrkanoon, Xiaolin Huang, Johan A. K. Suykens:
Indefinite kernel spectral learning. Pattern Recognit. 78: 144-153 (2018) - [j149]Bertrand Gauthier, Johan A. K. Suykens:
Optimal Quadrature-Sparsification for Integral Operator Approximation. SIAM J. Sci. Comput. 40(5): A3636-A3674 (2018) - [j148]Xiaolin Huang, Johan A. K. Suykens, Shuning Wang, Joachim Hornegger, Andreas K. Maier:
Classification With Truncated $\ell _{1}$ Distance Kernel. IEEE Trans. Neural Networks Learn. Syst. 29(5): 2025-2030 (2018) - [j147]Siamak Mehrkanoon, Johan A. K. Suykens:
Regularized Semipaired Kernel CCA for Domain Adaptation. IEEE Trans. Neural Networks Learn. Syst. 29(7): 3199-3213 (2018) - [j146]Carlos M. Alaíz, Michaël Fanuel, Johan A. K. Suykens:
Convex Formulation for Kernel PCA and Its Use in Semisupervised Learning. IEEE Trans. Neural Networks Learn. Syst. 29(8): 3863-3869 (2018) - [j145]Zhongming Chen, Kim Batselier, Johan A. K. Suykens, Ngai Wong:
Parallelized Tensor Train Learning of Polynomial Classifiers. IEEE Trans. Neural Networks Learn. Syst. 29(10): 4621-4632 (2018) - [c160]Saverio Salzo, Lorenzo Rosasco, Johan A. K. Suykens:
Solving lp-norm regularization with tensor kernels. AISTATS 2018: 1655-1663 - [c159]Hussain Kazmi, Johan A. K. Suykens, Johan Driesen:
Valuing Knowledge, Information and Agency in Multi-agent Reinforcement Learning: A Case Study in Smart Buildings. AAMAS 2018: 585-587 - [c158]Qinghua Tao, Jun Xu, Johan A. K. Suykens, Shuning Wang:
Fast Adaptive Hinging Hyperplanes. CDC 2018: 1482-1487 - [c157]Siamak Mehrkanoon, Matthew B. Blaschko, Johan A. K. Suykens:
Shallow and Deep Models for Domain Adaptation problems. ESANN 2018 - [c156]Joachim Schreurs, Johan A. K. Suykens:
Generative Kernel PCA. ESANN 2018 - [c155]Lynn Houthuys, Johan A. K. Suykens:
Tensor Learning in Multi-view Kernel PCA. ICANN (2) 2018: 205-215 - [c154]Zahra Karevan, Lynn Houthuys, Johan A. K. Suykens:
Weighted Multi-view Deep Neural Networks for Weather Forecasting. ICANN (3) 2018: 489-499 - [i27]Hussain Kazmi, Johan A. K. Suykens, Johan Driesen:
Valuing knowledge, information and agency in Multi-agent Reinforcement Learning: a case study in smart buildings. CoRR abs/1803.03491 (2018) - [i26]Anna Marconato, Jonas Sjöberg, Johan A. K. Suykens, Johan Schoukens:
Improved Initialization for Nonlinear State-Space Modeling. CoRR abs/1804.08654 (2018) - [i25]Fanghui Liu, Lei Shi, Xiaolin Huang, Jie Yang, Johan A. K. Suykens:
Generalization Properties of hyper-RKHS and its Application to Out-of-Sample Extensions. CoRR abs/1809.09910 (2018) - [i24]Zahra Karevan, Johan A. K. Suykens:
Spatio-temporal Stacked LSTM for Temperature Prediction in Weather Forecasting. CoRR abs/1811.06341 (2018) - 2017
- [j144]Rocco Langone, Johan A. K. Suykens:
Fast kernel spectral clustering. Neurocomputing 268: 27-33 (2017) - [j143]Rocco Langone, Johan A. K. Suykens:
Supervised aggregated feature learning for multiple instance classification. Inf. Sci. 375: 234-245 (2017) - [j142]Johan A. K. Suykens:
Deep Restricted Kernel Machines Using Conjugate Feature Duality. Neural Comput. 29(8): 2123-2163 (2017) - [j141]Haibo He, Robert Haas, Jun Fu, Barbara Hammer, Daniel W. C. Ho, Fakhri Karray, Dhireesha Kudithipudi, José Antonio Lozano, Teresa Bernarda Ludermir, Jacek Mandziuk, Stefano Melacci, Antonio Paiva, Hong Qiao, Alain Rakotomamonjy, Shiliang Sun, Johan A. K. Suykens, Meng Wang:
Editorial: A Successful Year and Looking Forward to 2017 and Beyond. IEEE Trans. Neural Networks Learn. Syst. 28(1): 2-7 (2017) - [j140]Xiaolin Huang, Lei Shi, Johan A. K. Suykens:
Solution Path for Pin-SVM Classifiers With Positive and Negative τ Values. IEEE Trans. Neural Networks Learn. Syst. 28(7): 1584-1593 (2017) - [c153]Giulio Bottegal, Ricardo Castro-Garcia, Johan A. K. Suykens:
On the identification of Wiener systems with polynomial nonlinearity. CDC 2017: 6475-6480 - [c152]Zahra Karevan, Yunlong Feng, Johan A. K. Suykens:
Moving Least Squares Support Vector Machines for weather temperature prediction. ESANN 2017 - [c151]Siamak Mehrkanoon, Andreas Zell, Johan A. K. Suykens:
Scalable Hybrid Deep Neural Kernel Networks. ESANN 2017 - [c150]Pantelis Sopasakis, Andreas Themelis, Johan A. K. Suykens, Panagiotis Patrinos:
A primal-dual line search method and applications in image processing. EUSIPCO 2017: 1065-1069 - [c149]Giulio Bottegal, Johan A. K. Suykens:
Probabilistic matrix factorization from quantized measurements. IJCNN 2017: 270-277 - [c148]Lynn Houthuys, Zahra Karevan, Johan A. K. Suykens:
Multi-view LS-SVM regression for black-box temperature prediction in weather forecasting. IJCNN 2017: 1102-1108 - [c147]Ricardo Castro-Garcia, Oscar Mauricio Agudelo, Johan A. K. Suykens:
MIMO hammerstein system identification using LS-SVM and steady state time response. SSCI 2017: 1-7 - [c146]Lynn Houthuys, Johan A. K. Suykens:
Unpaired multi-view kernel spectral clustering. SSCI 2017: 1-7 - [i23]Carlos M. Alaíz, Johan A. K. Suykens:
Modified Frank-Wolfe Algorithm for Enhanced Sparsity in Support Vector Machine Classifiers. CoRR abs/1706.05928 (2017) - [i22]Michaël Fanuel, Antoine Aspeel, Jean-Charles Delvenne, Johan A. K. Suykens:
Positive semi-definite embedding for dimensionality reduction and out-of-sample extensions. CoRR abs/1711.07271 (2017) - 2016
- [j139]