2026 Johan A. K. Suykens: Computer Science Researcher – H-Index, Publications & Awards

Discipline name	D-Index	World Ranking	Current World Ranking	National Ranking	Current National Ranking	Publications	Citations
Computer Science	93	503	487	4	4	736	53705

Research.com Recognitions

2026 - Research.com Computer Science in Belgium Leader Award
2025 - Research.com Computer Science in Belgium Leader Award
2023 - Research.com Computer Science in Belgium Leader Award
2022 - Research.com Computer Science in Belgium Leader Award
2015 - IEEE Fellow For developing the least squares support vector machines

Overview

Johan A. K. Suykens is affiliated with KU Leuven in Belgium and has contributed extensively to the field of Computer Science, with a primary focus on Artificial Intelligence. Their scholarly output spans various subfields, including Computer Vision and Pattern Recognition, Computational Mechanics, Signal Processing, and Statistical and Nonlinear Physics.

Their research topics cover diverse areas within machine learning and neural networks. Key topics include Neural Networks and Applications, Generative Adversarial Networks and Image Synthesis, Face and Expression Recognition, Machine Learning and Extreme Learning Machines (ELM), Sparse and Compressive Sensing Techniques, Machine Learning and Data Classification, and Model Reduction and Neural Networks.

Johan A. K. Suykens has published numerous papers in a variety of venues. Frequent publication venues include arXiv (Cornell University), Neural Networks, IEEE Transactions on Pattern Analysis and Machine Intelligence, SIAM Journal on Mathematics of Data Science, and Lirias (KU Leuven).

Recent scholarly papers authored or co-authored by Suykens include:

"Transductive LSTM for time-series prediction: An application to weather forecasting" (2020) in Neural Networks
"Random Features for Kernel Approximation: A Survey on Algorithms, Theory, and Beyond" (2021) in IEEE Transactions on Pattern Analysis and Machine Intelligence
"Transfer learning in demand response: A review of algorithms for data-efficient modelling and control" (2021) in Energy and AI
"A novel neural grey system model with Bayesian regularization and its applications" (2021) in Neurocomputing
"Piecewise linear neural networks and deep learning" (2022) in Nature Reviews Methods Primers

Collaborations are a notable aspect of Suykens' research career, with frequent co-authors including Joachim Schreurs, Panagiotis Patrinos, Michaël Fanuel, Xiaolin Huang, and Fanghui Liu.

Suykens has been recognized with professional distinctions such as the IEEE Fellow award in 2015 for contributions to the development of least squares support vector machines.

Best Publications

Least Squares Support Vector Machine Classifiers

J. A. K. Suykens;J. Vandewalle

13396
Citations
Least Squares Support Vector Machines

Johan A K Suykens;Tony Van Gestel;Jos De Brabanter;Bart De Moor

6031
Citations
Weighted least squares support vector machines: robustness and sparse approximation

J.A.K. Suykens;J. De Brabanter;L. Lukas;J. Vandewalle

1838
Citations
Benchmarking state-of-the-art classification algorithms for credit scoring

B Baesens;T Van Gestel;S Viaene;M Stepanova

1268
Citations
Benchmarking Least Squares Support Vector Machine Classifiers

Tony Van Gestel;Johan A. K. Suykens;Bart Baesens;Stijn Viaene

942
Citations
Optimal control by least squares support vector machines

J. A. K. Suykens;J. Vandewalle;B. De Moor

776
Citations
Financial time series prediction using least squares support vector machines within the evidence framework

T. Van Gestel;J.A.K. Suykens;D.-E. Baestaens;A. Lambrechts

766
Citations
Artificial Neural Networks for Modelling and Control of Non-Linear Systems

Johan A. K. Suykens;Joos P. L. Vandewalle;B. L. de Moor

674
Citations
Recurrent least squares support vector machines

J.A.K. Suykens;J. Vandewalle

602
Citations
Nonlinear modelling and support vector machines

J.A.K. Suykens

525
Citations
Sparse approximation using least squares support vector machines

J.A.K. Suykens;L. Lukas;J. Vandewalle

515
Citations
Transductive LSTM for time-series prediction: An application to weather forecasting.

Zahra Karevan;Johan A.K. Suykens

497
Citations
Bayesian framework for least-squares support vector machine classifiers, Gaussian processes, and kernel fisher discriminant analysis

T. Van Gestel;J. A. K. Suykens;G. Lanckriet;A. Lambrechts

409
Citations
Coupled Simulated Annealing

S. Xavier-de-Souza;J.A.K. Suykens;J. Vandewalle;D. Bolle

390
Citations
A tutorial on support vector machine-based methods for classification problems in chemometrics.

Jan Luts;Fabian Ojeda;Raf Van de Plas;Bart De Moor

389
Citations
True random bit generation from a double-scroll attractor

M.E. Yalcin;J.A.K. Suykens;J. Vandewalle

376
Citations
LS-SVMlab Toolbox User's Guide version 1.7

Kris De Brabanter;Peter Karsmakers;Fabian Ojeda;Carlos Alzate

338
Citations
Support Vector Machine Classifier With Pinball Loss

Xiaolin Huang;Lei Shi;Johan A. K. Suykens

335
Citations
Least squares support vector machine classifiers: a large scale algorithm

J. Suykens;L. Lukas;Paul Van Dooren;J. Vandewalle

332
Citations
Handling missing values in support vector machine classifiers

K. Pelckmans;J. De Brabanter;J. A. K. Suykens;B. De Moor

290
Citations
Introduction to Machine Learning

Johan A.K. Suykens

157
Citations

Frequent Co-Authors

Joos Vandewalle KU Leuven

Bart De Moor KU Leuven

Sabine Van Huffel KU Leuven

Xiaolin Huang Shanghai Jiao Tong University

Leon O. Chua University of California, Berkeley

Bart Baesens KU Leuven

Jan Vanthienen KU Leuven

Johan Schoukens Eindhoven University of Technology

Johan Driesen KU Leuven

Yves Moreau KU Leuven

External Links

Wikipedia of Johan A. K. Suykens Personal website of Johan A. K. Suykens Google Scholar page

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