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Johan A. K. Suykens: [Publications] [Author Rank by year] [Co-authors] [Prefers] [Cites] [Cited by]

Publications of Author

  1. Chuan Lu, Tony Van Gestel, Johan A. K. Suykens, Sabine Van Huffel, Dirk Timmerman, Ignace Vergote
    Classification of Ovarian Tumors Using Bayesian Least Squares Support Vector Machines. [Citation Graph (0, 0)][DBLP]
    AIME, 2003, pp:219-228 [Conf]
  2. Nathalie Pochet, Frizo A. L. Janssens, Frank De Smet, Kathleen Marchal, Ignace Vergote, Johan A. K. Suykens, Bart De Moor
    M@CBETH: Optimizing Clinical Microarray Classification. [Citation Graph (0, 0)][DBLP]
    CSB Workshops, 2005, pp:89-90 [Conf]
  3. Lieveke Ameye, Chuan Lu, Lukas Lukas, Jos De Brabanter, Johan A. K. Suykens, Sabine Van Huffel, Hans Daniels, Gunnar Naulaers, Hugo Devlieger
    Prediction of mental development of preterm newborns at birth time using LS-SVM. [Citation Graph (0, 0)][DBLP]
    ESANN, 2002, pp:167-172 [Conf]
  4. Tony Van Gestel, Johan A. K. Suykens, Bart De Moor, Joos Vandewalle
    Automatic relevance determination for Least Squares Support Vector Machines classifiers. [Citation Graph (0, 0)][DBLP]
    ESANN, 2001, pp:13-18 [Conf]
  5. Luc Hoegaerts, Johan A. K. Suykens, Joos Vandewalle, Bart De Moor
    Kernel PLS variants for regression. [Citation Graph (0, 0)][DBLP]
    ESANN, 2003, pp:200-208 [Conf]
  6. Lukas Lukas, Andy Devos, Johan A. K. Suykens, Leentje Vanhamme, Sabine Van Huffel, Anne Rosemary Tate, Carles Majós, Carles Arús
    The use of LS-SVM in the classification of brain tumors based on Magnetic Resonance Spectroscopy signals. [Citation Graph (0, 0)][DBLP]
    ESANN, 2002, pp:131-136 [Conf]
  7. Kristiaan Pelckmans, Johan A. K. Suykens, Bart De Moor
    Sparse LS-SVMs using additive regularization with a penalized validation criterion. [Citation Graph (0, 0)][DBLP]
    ESANN, 2004, pp:435-440 [Conf]
  8. Johan A. K. Suykens, Lukas Lukas, Joos Vandewalle
    Sparse least squares Support Vector Machine classifiers. [Citation Graph (0, 0)][DBLP]
    ESANN, 2000, pp:37-42 [Conf]
  9. Johan A. K. Suykens, Joos Vandewalle
    The K.U.Leuven competition data: a challenge for advanced neural network techniques. [Citation Graph (0, 0)][DBLP]
    ESANN, 2000, pp:299-304 [Conf]
  10. Johan A. K. Suykens, Joos Vandewalle
    Improved generalization ability of neurocontrollers by imposing NLq stability constraints. [Citation Graph (0, 0)][DBLP]
    ESANN, 1998, pp:99-104 [Conf]
  11. Jos De Brabanter, Kristiaan Pelckmans, Johan A. K. Suykens, Joos Vandewalle
    Robust Cross-Validation Score Function for Non-linear Function Estimation. [Citation Graph (0, 0)][DBLP]
    ICANN, 2002, pp:713-719 [Conf]
  12. Tony Van Gestel, Johan A. K. Suykens, Jos De Brabanter, Bart De Moor, Joos Vandewalle
    Kernel Canonical Correlation Analysis and Least Squares Support Vector Machines. [Citation Graph (0, 0)][DBLP]
    ICANN, 2001, pp:384-389 [Conf]
  13. Bart Hamers, Johan A. K. Suykens, Bart De Moor
    Compactly Supported RBF Kernels for Sparsifying the Gram Matrix in LS-SVM Regression Models. [Citation Graph (0, 0)][DBLP]
    ICANN, 2002, pp:720-726 [Conf]
  14. Kristiaan Pelckmans, Johan A. K. Suykens, Bart De Moor
    Componentwise Support Vector Machines for Structure Detection. [Citation Graph (0, 0)][DBLP]
    ICANN (2), 2005, pp:643-648 [Conf]
  15. Luc Hoegaerts, Johan A. K. Suykens, Joos Vandewalle, Bart De Moor
    A Comparison of Pruning Algorithms for Sparse Least Squares Support Vector Machines. [Citation Graph (0, 0)][DBLP]
    ICONIP, 2004, pp:1247-1253 [Conf]
  16. Kristiaan Pelckmans, Johan A. K. Suykens, Bart De Moor
    Morozov, Ivanov and Tikhonov Regularization Based LS-SVMs. [Citation Graph (0, 0)][DBLP]
    ICONIP, 2004, pp:1216-1222 [Conf]
  17. Johan A. K. Suykens, Joos Vandewalle
    Generalized Cellular Neural Networks Represented in he NLq Framework. [Citation Graph (0, 0)][DBLP]
    ISCAS, 1995, pp:645-648 [Conf]
  18. Johan A. K. Suykens, Mustak E. Yalcin, Joos Vandewalle
    Coupled chaotic simulated annealing processes. [Citation Graph (0, 0)][DBLP]
    ISCAS (3), 2003, pp:582-585 [Conf]
  19. Mustak E. Yalcin, Johan A. K. Suykens, Joos Vandewalle
    A double scroll based true random bit generator. [Citation Graph (0, 0)][DBLP]
    ISCAS (4), 2004, pp:581-584 [Conf]
  20. Mustak E. Yalcin, Johan A. K. Suykens, Joos Vandewalle
    Spatiotemporal pattern formation in the ACE16k CNN chip. [Citation Graph (0, 0)][DBLP]
    ISCAS (6), 2005, pp:5814-5817 [Conf]
  21. Mustak E. Yalcin, Johan A. K. Suykens, Joos Vandewalle
    On the realization of n-scroll attractors. [Citation Graph (0, 0)][DBLP]
    ISCAS (5), 1999, pp:483-486 [Conf]
  22. Marcelo Espinoza, Johan A. K. Suykens, Bart De Moor
    Load Forecasting Using Fixed-Size Least Squares Support Vector Machines. [Citation Graph (0, 0)][DBLP]
    IWANN, 2005, pp:1018-1026 [Conf]
  23. Stijn Viaene, Bart Baesens, Tony Van Gestel, Johan A. K. Suykens, Dirk Van den Poel, Jan Vanthienen, Bart De Moor, Guido Dedene
    Knowledge Discovery Using Least Squares Support Vector Machine Classifiers: A Direct Marketing Case. [Citation Graph (0, 0)][DBLP]
    PKDD, 2000, pp:657-664 [Conf]
  24. Tijl De Bie, Johan A. K. Suykens, Bart De Moor
    Learning from General Label Constraints. [Citation Graph (0, 0)][DBLP]
    SSPR/SPR, 2004, pp:671-679 [Conf]
  25. Chuan Lu, Tony Van Gestel, Johan A. K. Suykens, Sabine Van Huffel, Ignace Vergote, Dirk Timmerman
    Preoperative prediction of malignancy of ovarian tumors using least squares support vector machines. [Citation Graph (0, 0)][DBLP]
    Artificial Intelligence in Medicine, 2003, v:28, n:3, pp:281-306 [Journal]
  26. Lukas Lukas, Andy Devos, Johan A. K. Suykens, Leentje Vanhamme, F. A. Howe, Carles Majós, A. Moreno-Torres, M. Van Der Graaf, Anne Rosemary Tate, Carles Arús, Sabine Van Huffel
    Brain tumor classification based on long echo proton MRS signals. [Citation Graph (0, 0)][DBLP]
    Artificial Intelligence in Medicine, 2004, v:31, n:1, pp:73-89 [Journal]
  27. Nathalie Pochet, Frizo A. L. Janssens, Frank De Smet, Kathleen Marchal, Johan A. K. Suykens, Bart De Moor
    M@CBETH: a microarray classification benchmarking tool. [Citation Graph (0, 0)][DBLP]
    Bioinformatics, 2005, v:21, n:14, pp:3185-3186 [Journal]
  28. Nathalie Pochet, Frank De Smet, Johan A. K. Suykens, Bart De Moor
    Systematic benchmarking of microarray data classification: assessing the role of non-linearity and dimensionality reduction. [Citation Graph (0, 0)][DBLP]
    Bioinformatics, 2004, v:20, n:17, pp:3185-3195 [Journal]
  29. Johan A. K. Suykens, Joos Vandewalle, Bart De Moor
    Intelligence and Cooperative Search by Coupled Local Minimizers [Citation Graph (0, 0)][DBLP]
    CoRR, 2002, v:0, n:, pp:- [Journal]
  30. Tony Van Gestel, Bart Baesens, Peter Van Dijcke, Joao Garcia, Johan A. K. Suykens, Jan Vanthienen
    A process model to develop an internal rating system: Sovereign credit ratings. [Citation Graph (0, 0)][DBLP]
    Decision Support Systems, 2006, v:42, n:2, pp:1131-1151 [Journal]
  31. Stijn Viaene, Bart Baesens, Tony Van Gestel, Johan A. K. Suykens, Dirk Van den Poel, Jan Vanthienen, Bart De Moor, Guido Dedene
    Knowledge discovery in a direct marketing case using least squares support vector machines. [Citation Graph (0, 0)][DBLP]
    Int. J. Intell. Syst., 2001, v:16, n:9, pp:1023-1036 [Journal]
  32. Michel Duhoux, Johan A. K. Suykens, Bart De Moor, Joos Vandewalle
    Improved Long-Term Temperature Prediction by Chaining of Neural Networks. [Citation Graph (0, 0)][DBLP]
    Int. J. Neural Syst., 2001, v:11, n:1, pp:1-10 [Journal]
  33. Luc Hoegaerts, Johan A. K. Suykens, Joos Vandewalle, Bart De Moor
    Subset based least squares subspace regression in RKHS. [Citation Graph (0, 0)][DBLP]
    Neurocomputing, 2005, v:63, n:, pp:293-323 [Journal]
  34. Kristiaan Pelckmans, Jos De Brabanter, Johan A. K. Suykens, Bart De Moor
    The differogram: Non-parametric noise variance estimation and its use for model selection. [Citation Graph (0, 0)][DBLP]
    Neurocomputing, 2005, v:69, n:1-3, pp:100-122 [Journal]
  35. Kristiaan Pelckmans, Johan A. K. Suykens, Bart De Moor
    Building sparse representations and structure determination on LS-SVM substrates. [Citation Graph (0, 0)][DBLP]
    Neurocomputing, 2005, v:64, n:, pp:137-159 [Journal]
  36. Johan A. K. Suykens, Jos De Brabanter, Lukas Lukas, Joos Vandewalle
    Weighted least squares support vector machines: robustness and sparse approximation. [Citation Graph (0, 0)][DBLP]
    Neurocomputing, 2002, v:48, n:1-4, pp:85-105 [Journal]
  37. Tony Van Gestel, Johan A. K. Suykens, Bart Baesens, Stijn Viaene, Jan Vanthienen, Guido Dedene, Bart De Moor, Joos Vandewalle
    Benchmarking Least Squares Support Vector Machine Classifiers. [Citation Graph (0, 0)][DBLP]
    Machine Learning, 2004, v:54, n:1, pp:5-32 [Journal]
  38. Kristiaan Pelckmans, Johan A. K. Suykens, Bart De Moor
    Additive Regularization Trade-Off: Fusion of Training and Validation Levels in Kernel Methods. [Citation Graph (0, 0)][DBLP]
    Machine Learning, 2006, v:62, n:3, pp:217-252 [Journal]
  39. Tony Van Gestel, Johan A. K. Suykens, Gert R. G. Lanckriet, Annemie Lambrechts, Bart De Moor, Joos Vandewalle
    Bayesian Framework for Least-Squares Support Vector Machine Classifiers, Gaussian Processes, and Kernel Fisher Discriminant Analysis. [Citation Graph (0, 0)][DBLP]
    Neural Computation, 2002, v:14, n:5, pp:1115-1147 [Journal]
  40. Kristiaan Pelckmans, Jos De Brabanter, Johan A. K. Suykens, Bart De Moor
    Handling missing values in support vector machine classifiers. [Citation Graph (0, 0)][DBLP]
    Neural Networks, 2005, v:18, n:5-6, pp:684-692 [Journal]
  41. Johan A. K. Suykens, Bart De Moor, Joos Vandewalle
    Static and dynamic stabilizing neural controllers, applicable to transition between equilibrium points. [Citation Graph (0, 0)][DBLP]
    Neural Networks, 1994, v:7, n:5, pp:819-831 [Journal]
  42. Johan A. K. Suykens, Bart De Moor, Joos Vandewalle
    NLq Theory: A Neural Control Framework with Global Asymptotic Stability Criteria. [Citation Graph (0, 0)][DBLP]
    Neural Networks, 1997, v:10, n:4, pp:615-637 [Journal]
  43. Johan A. K. Suykens, Joos Vandewalle, Bart De Moor
    Optimal control by least squares support vector machines. [Citation Graph (0, 0)][DBLP]
    Neural Networks, 2001, v:14, n:1, pp:23-35 [Journal]
  44. Luc Hoegaerts, Lieven De Lathauwer, Ivan Goethals, Johan A. K. Suykens, Joos Vandewalle, Bart De Moor
    Efficiently updating and tracking the dominant kernel principal components. [Citation Graph (0, 0)][DBLP]
    Neural Networks, 2007, v:20, n:2, pp:220-229 [Journal]
  45. Tony Van Gestel, Johan A. K. Suykens, Gert R. G. Lanckriet, Annemie Lambrechts, Bart De Moor, Joos Vandewalle
    Multiclass LS SVMs Moderated Outputs and Coding Decoding Schemes. [Citation Graph (0, 0)][DBLP]
    Neural Processing Letters, 2002, v:15, n:1, pp:45-58 [Journal]
  46. Kristiaan Pelckmans, Marcelo Espinoza, Jos De Brabanter, Johan A. K. Suykens, Bart De Moor
    Primal-Dual Monotone Kernel Regression. [Citation Graph (0, 0)][DBLP]
    Neural Processing Letters, 2005, v:22, n:2, pp:171-182 [Journal]
  47. Johan A. K. Suykens, Philippe Lemmerling, W. Favoreel, Bart De Moor, M. Crepel, P. Briol
    Modelling the Belgian Gas Consumption Using Neural Networks. [Citation Graph (0, 0)][DBLP]
    Neural Processing Letters, 1996, v:4, n:3, pp:157-166 [Journal]
  48. Johan A. K. Suykens, Joos Vandewalle
    Least Squares Support Vector Machine Classifiers. [Citation Graph (0, 0)][DBLP]
    Neural Processing Letters, 1999, v:9, n:3, pp:293-300 [Journal]
  49. Johan A. K. Suykens, Herman Verrelst, Joos Vandewalle
    On-Line Learning Fokker-Planck Machine. [Citation Graph (0, 0)][DBLP]
    Neural Processing Letters, 1998, v:7, n:2, pp:81-89 [Journal]
  50. Tony Van Gestel, Bart Baesens, Johan A. K. Suykens, Dirk Van den Poel, Dirk-Emma Baestaens, Marleen Willekens
    Bayesian kernel based classification for financial distress detection. [Citation Graph (0, 0)][DBLP]
    European Journal of Operational Research, 2006, v:172, n:3, pp:979-1003 [Journal]
  51. Ben Van Calster, Jan Luts, Johan A. K. Suykens, George Condous, Tom Bourne, Dirk Timmerman, Sabine Van Huffel
    Comparing Methods for Multi-class Probabilities in Medical Decision Making Using LS-SVMs and Kernel Logistic Regression. [Citation Graph (0, 0)][DBLP]
    ICANN (2), 2007, pp:139-148 [Conf]
  52. Mustak E. Yalcin, Johan A. K. Suykens, Joos Vandewalle
    Multi-scroll and hypercube attractors from Josephson junctions. [Citation Graph (0, 0)][DBLP]
    ISCAS, 2006, pp:- [Conf]
  53. Kristiaan Pelckmans, Jos De Brabanter, Johan A. K. Suykens, Bart De Moor
    Support and Quantile Tubes [Citation Graph (0, 0)][DBLP]
    CoRR, 2007, v:0, n:, pp:- [Journal]
  54. Kristiaan Pelckmans, Ivan Goethals, Jos De Brabanter, Johan A. K. Suykens, Bart De Moor
    Componentwise Least Squares Support Vector Machines [Citation Graph (0, 0)][DBLP]
    CoRR, 2005, v:0, n:, pp:- [Journal]

  55. Differentiation between brain metastases and glioblastoma multiforme based on MRI, MRS and MRSI. [Citation Graph (, )][DBLP]


  56. Convex optimization for the design of learning machines. [Citation Graph (, )][DBLP]


  57. Survival SVM: a practical scalable algorithm. [Citation Graph (, )][DBLP]


  58. Quadratically Constrained Quadratic Programming for Subspace Selection in Kernel Regression Estimation. [Citation Graph (, )][DBLP]


  59. MINLIP: Efficient Learning of Transformation Models. [Citation Graph (, )][DBLP]


  60. Robustness of Kernel Based Regression: A Comparison of Iterative Weighting Schemes. [Citation Graph (, )][DBLP]


  61. Identifying Customer Profiles in Power Load Time Series Using Spectral Clustering. [Citation Graph (, )][DBLP]


  62. Kernel-Based Learning from Infinite Dimensional 2-Way Tensors. [Citation Graph (, )][DBLP]


  63. Feature Extraction and Classification of EEG Signals for Rapid P300 Mind Spelling. [Citation Graph (, )][DBLP]


  64. Multi-class kernel logistic regression: a fixed-size implementation. [Citation Graph (, )][DBLP]


  65. Variable selection by rank-one updates for least squares support vector machines. [Citation Graph (, )][DBLP]


  66. ICA through an LS-SVM based Kernel CCA Measure for Independence. [Citation Graph (, )][DBLP]


  67. A Weighted Kernel PCA Formulation with Out-of-Sample Extensions for Spectral Clustering Methods. [Citation Graph (, )][DBLP]


  68. Sparse kernel models for spectral clustering using the incomplete Cholesky decomposition. [Citation Graph (, )][DBLP]


  69. Feature Selection in Survival Least Squares Support Vector Machines with Maximal Variation Constraints. [Citation Graph (, )][DBLP]


  70. An empirical assessment of kernel type performance for least squares support vector machine classifiers. [Citation Graph (, )][DBLP]


  71. P300 Detection Based on Feature Extraction in On-line Brain-Computer Interface. [Citation Graph (, )][DBLP]


  72. A Risk Minimization Principle for a Class of Parzen Estimators. [Citation Graph (, )][DBLP]


  73. Semi-supervised Learning of Sparse Linear Models in Mass Spectral Imaging. [Citation Graph (, )][DBLP]


  74. Transductive Rademacher Complexities for Learning Over a Graph. [Citation Graph (, )][DBLP]


  75. A proximal center-based decomposition method for multi-agent convex optimization. [Citation Graph (, )][DBLP]


  76. Application of the proximal center decomposition method to distributed model predictive control. [Citation Graph (, )][DBLP]


  77. Distributed nonlinear optimal control using sequential convex programming and smoothing techniques. [Citation Graph (, )][DBLP]


  78. Robustness analysis for Least Squares kernel based regression: an optimization approach. [Citation Graph (, )][DBLP]


  79. A combined MRI and MRSI based multiclass system for brain tumour recognition using LS-SVMs with class probabilities and feature selection. [Citation Graph (, )][DBLP]


  80. L2-norm multiple kernel learning and its application to biomedical data fusion. [Citation Graph (, )][DBLP]


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