J
Johan A. K. Suykens
Researcher at Katholieke Universiteit Leuven
Publications - 717
Citations - 38265
Johan A. K. Suykens is an academic researcher from Katholieke Universiteit Leuven. The author has contributed to research in topics: Support vector machine & Least squares support vector machine. The author has an hindex of 79, co-authored 693 publications receiving 34482 citations. Previous affiliations of Johan A. K. Suykens include University of California, Berkeley & Federico Santa María Technical University.
Papers
More filters
Journal ArticleDOI
Least Squares Support Vector Machine Classifiers
TL;DR: A least squares version for support vector machine (SVM) classifiers that follows from solving a set of linear equations, instead of quadratic programming for classical SVM's.
Book
Least Squares Support Vector Machines
TL;DR: Support Vector Machines Basic Methods of Least Squares Support Vector Machines Bayesian Inference for LS-SVM Models Robustness Large Scale Problems LS- sVM for Unsupervised Learning LS- SVM for Recurrent Networks and Control.
Journal ArticleDOI
Weighted least squares support vector machines: robustness and sparse approximation
TL;DR: The methods of this paper are illustrated for RBF kernels and demonstrate how to obtain robust estimates with selection of an appropriate number of hidden units, in the case of outliers or non-Gaussian error distributions with heavy tails.
Journal ArticleDOI
Benchmarking state-of-the-art classification algorithms for credit scoring
TL;DR: It is found that both the LS-SVM and neural network classifiers yield a very good performance, but also simple classifiers such as logistic regression and linear discriminant analysis perform very well for credit scoring.
Journal ArticleDOI
Benchmarking Least Squares Support Vector Machine Classifiers
Tony Van Gestel,Johan A. K. Suykens,Bart Baesens,Stijn Viaene,Jan Vanthienen,Guido Dedene,Bart De Moor,Joos Vandewalle +7 more
TL;DR: Both the SVM and LS-SVM classifier with RBF kernel in combination with standard cross-validation procedures for hyperparameter selection achieve comparable test set performances, consistently very good when compared to a variety of methods described in the literature.