Optimal control by least squares support vector machines von Suykens, J.A.K., Vandewalle, J., De Moor, B.

Volltext

Benchmarking state-of-the-art classification algorithms for credit scoring von Baesens, B, Van Gestel, T, Viaene, S, Stepanova, M, Suykens, J, Vanthienen, J

Volltext

Application of a Smoothing Technique to Decomposition in Convex Optimization von Necoara, I., Suykens, J.

Volltext

Optimized fixed-size kernel models for large data sets von De Brabanter, K., De Brabanter, J., Suykens, J.A.K., De Moor, B.

Volltext

Improved performance on high-dimensional survival data by application of Survival-SVM von Van Belle, V., Pelckmans, K., Van Huffel, S., Suykens, J. A. K.

Volltext

Deformed Laplacians and spectral ranking in directed networks von Fanuel, M., Suykens, J.A.K.

Volltext

Interior-Point Lagrangian Decomposition Method for Separable Convex Optimization von Necoara, I., Suykens, J. A. K.

Volltext

Least squares support vector machine classifiers von SUYKENS, J. A. K, VANDEWALLE, J

Volltext

Extending Newton's law from nonlocal-in-time kinetic energy von Suykens, J.A.K.

Volltext

Bayesian Framework for Least-Squares Support Vector Machine Classifiers, Gaussian Processes, and Kernel Fisher Discriminant Analysis von Gestel, T. Van, Suykens, J. A. K., Lanckriet, G., Lambrechts, A., Moor, B. De, Vandewalle, J.

Volltext

Financial time series prediction using least squares support vector machines within the evidence framework von Van Gestel, T., Suykens, J.A.K., Baestaens, D.-E., Lambrechts, A., Lanckriet, G., Vandaele, B., De Moor, B., Vandewalle, J.

Volltext

Kernel Component Analysis Using an Epsilon-Insensitive Robust Loss Function von Alzate, C., Suykens, J.

Volltext

Reducing the Number of Support Vectors of SVM Classifiers Using the Smoothed Separable Case Approximation von Geebelen, D., Suykens, J. A. K., Vandewalle, J.

Volltext

Confidence bands for least squares support vector machine classifiers: A regression approach von De Brabanter, K., Karsmakers, P., De Brabanter, J., Suykens, J.A.K., De Moor, B.

Volltext

A support vector machine formulation to PCA analysis and its kernel version von Suykens, J.A.K., Van Gestel, T., Vandewalle, J., De Moor, B.

Volltext

Efficiently updating and tracking the dominant kernel principal components von Hoegaerts, L., De Lathauwer, L., Goethals, I., Suykens, J.A.K., Vandewalle, J., De Moor, B.

Volltext

Towards the detection of error-related potentials and its integration in the context of a P300 speller brain–computer interface von Combaz, A., Chumerin, N., Manyakov, N.V., Robben, A., Suykens, J.A.K., Van Hulle, M.M.

Volltext

Sequentially activated groups in neural networks von Komarov, M. A, Osipov, G. V, Suykens, J. A. K

Volltext

Additive survival least-squares support vector machines von Van Belle, V., Pelckmans, K., Suykens, J. A. K., Van Huffel, S.

Volltext

Support vector machines versus logistic regression: improving prospective performance in clinical decision‐making von Pochet, N. L. M. M., Suykens, J. A. K.

Volltext