Support Vector Machines is a powerful methodology for solving problems in nonlinear classification, function estimation and density estimation which has also led to many other recent developments in kernel based methods in general. Originally, it has been introduced within the context of statistical learning theory and structural risk minimization. In the methods one solves convex optimization problems, typically quadratic programs. Least Squares Support Vector Machines (LS-SVM) are reformulations to the standard SVMs which lead to solving linear KKT systems. LS-SVMs are closely related to regularization networks and Gaussian processes but additionally emphasize and exploit primal-dual interpretations. Links between kernel versions of classical pattern recognition algorithms such as kernel Fisher discriminant analysis and extensions to unsupervised learning, recurrent networks and control are available. Robustness, sparseness and weightings can be incorporated into LS-SVMs where needed and a Bayesian framework with three levels of inference has been developed. LS-SVM based primal-dual formulations have been given to kernel PCA, kernel CCA and kernel PLS. Recent developments are in kernel spectral clustering, data visualization and dimensionality reduction, and survival analysis. For very large scale problems a method of Fixed Size LS-SVM is proposed. The present LS-SVMlab toolbox contains Matlab/C implementations for a number of LS-SVM algorithms.

- NEW! - Latest version: LS-SVMlab v1.8 (August 16, 2011) - NEW! -

Book reference:
J.A.K. Suykens, T. Van Gestel, J. De Brabanter, B. De Moor, J. Vandewalle, Least Squares Support Vector Machines, World Scientific, Singapore, 2002 (ISBN 981-238-151-1)

Presentations:

Invited talk at ICCHA4 2011, Hong Kong [pdf]

Tutorial at IEEE World Congress on Computational Intelligence WCCI 2010, Barcelona Spain [Part I - pdf] [Part II - pdf]

Invited talk at SYNCLINE 2010, Bad Honnef Germany [pdf1] [paper-pdf]

Semi-plenary talk at Symposium on System Identification SYSID 2009, Saint-Malo [pdf]

Plenary talk at International Conference on Multivariate Approximation, 2008 Bommerholz [pdf-1/page] [pdf-4/page]

Plenary talk at MFO Workshop on Learning Theory and Approximation, 2008 Oberwolfach (organizers: K. Jetter, S. Smale, D.-X. Zhou) [pdf-1/page] [pdf-4/page] [paper-pdf]

Invited tutorial: International Conference on Artificial Neural Networks ICANN 2007 Porto Portugal: "Support Vector Machines and Kernel Based Learning" [pdf-1/page] [pdf-4/page]

Invited talk at International Conference on Computational Harmonic Analysis 2007 Shanghai China: "Data visualization and dimensionality reduction using kernel maps with a reference point" [pdf] [paper-pdf]

Invited talk at International Workshop on Current Challenges in Kernel Methods CCKM 2006 Brussels Belgium: "Engineering Kernel Machines" [pdf-1/page] [pdf-4/page]

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Page maintained by Kristiaan Pelckmans and Johan Suykens. Last update, Nov 4 2011.