LS-SVMlab: a matlab/c toolbox for least squares support vector machines. 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.

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  1. Upadhyay, R.; Padhy, P.K.; Kankar, P.K.: Application of S-transform for automated detection of vigilance level using EEG signals (2016)
  2. Calvo-Rolle, José Luis; Quintian-Pardo, Héctor; Corchado, Emilio; del Carmen Meizoso-López, María; Ferreiro García, Ramón: Simplified method based on an intelligent model to obtain the extinction angle of the current for a single-phase half wave controlled rectifier with resistive and inductive load (2015)
  3. Garg, A.; Tai, K.; Gupta, A.K.: A modified multi-gene genetic programming approach for modelling true stress of dynamic strain aging regime of austenitic stainless steel 304 (2014)
  4. Grigorievskiy, Alexander; Miche, Yoan; Ventelä, Anne-Mari; Séverin, Eric; Lendasse, Amaury: Long-term time series prediction using OP-ELM (2014)
  5. Signoretto, Marco; Dinh, Quoc Tran; De Lathauwer, Lieven; Suykens, Johan A.K.: Learning with tensors: a framework based on convex optimization and spectral regularization (2014)
  6. Xia, Xiao-Lei; Jiao, Weidong; Li, Kang; Irwin, George: A novel sparse least squares support vector machines (2013)
  7. Xia, Xiao-Lei; Qian, Suxiang; Liu, Xueqin; Xing, Huanlai: Efficient model selection for sparse least-square SVMs (2013)
  8. Signoretto, Marco; De Lathauwer, Lieven; Suykens, Johan A.K.: A kernel-based framework to tensorial data analysis (2011)
  9. Baylar, A.; Batan, M.: Usage of artificial intelligence methods in free flowing gated closed conduits for estimation of oxygen transfer efficiency (2010)
  10. Ghorai, Santanu; Hossain, Shaikh Jahangir; Mukherjee, Anirban; Dutta, Pranab K.: Newton’s method for nonparallel plane proximal classifier with unity norm hyperplanes (2010)
  11. Ghorai, Santanu; Mukherjee, Anirban; Dutta, Pranab K.: Nonparallel plane proximal classifier (2009)
  12. Ben Mabrouk, Anouar; Kortas, Hedi; Dhifaoui, Zouhaier: A wavelet support vector machine coupled method for time series prediction (2008)
  13. Carbonneau, Real; Laframboise, Kevin; Vahidov, Rustam: Application of machine learning techniques for supply chain demand forecasting (2008)
  14. Wong, P.K.; Tam, L.M.; Li, K.; Wong, H.C.: Automotive engine idle speed control optimization using least squares support vector machine and genetic algorithm (2008)
  15. Centeno, Tonatiuh Peña; Lawrence, Neil D.: Optimising kernel parameters and regularisation coefficients for non-linear discriminant analysis (2006)