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This book focuses on Least Squares Support Vector Machines (LS-SVMs) which are reformulations to standard SVMs. LS-SVMs are closely related to regularization networks and Gaussian processes but additionally emphasize and exploit primal-dual interpretations from optimization theory. The authors explain the natural links between LS-SVM classifiers and kernel Fisher discriminant analysis. Bayesian inference of LS-SVM models is discussed, together with methods for imposing sparseness and employing robust statistics. The framework is further extended towards unsupervised learning by considering P

517.5 --- 681.3*C12 --- 681.3*G12 --- 681.3*G12 Approximation: chebyshev elementary function least squares linear approximation minimax approximation and algorithms nonlinear and rational approximation spline and piecewise polynomial approximation (Numerical analysis) --- Approximation: chebyshev elementary function least squares linear approximation minimax approximation and algorithms nonlinear and rational approximation spline and piecewise polynomial approximation (Numerical analysis) --- 517.5 Theory of functions --- Theory of functions --- 681.3*C12 Multiple data stream architectures (multiprocessors): MIMD SIMD pipeline and parallel processors array-, vector-, associative processors interconnection architectures: common bus, multiport memory, crossbar switch --- Multiple data stream architectures (multiprocessors): MIMD SIMD pipeline and parallel processors array-, vector-, associative processors interconnection architectures: common bus, multiport memory, crossbar switch --- Moindres carrés --- Method of least squares --- Squares, Least --- Curve fitting --- Triangulation --- Multiple data stream architectures (multiprocessors): MIMD; SIMD; pipeline and parallel processors; array-, vector-, associative processors; interconnection architectures: common bus, multiport memory, crossbar switch --- Approximation: chebyshev; elementary function; least squares; linear approximation; minimax approximation and algorithms; nonlinear and rational approximation; spline and piecewise polynomial approximation (Numerical analysis) --- 681.3*G12 Approximation: chebyshev; elementary function; least squares; linear approximation; minimax approximation and algorithms; nonlinear and rational approximation; spline and piecewise polynomial approximation (Numerical analysis) --- 681.3*C12 Multiple data stream architectures (multiprocessors): MIMD; SIMD; pipeline and parallel processors; array-, vector-, associative processors; interconnection architectures: common bus, multiport memory, crossbar switch --- Moindres carrés --- Machine learning. --- Algorithms. --- Kernel functions. --- Least squares. --- Functions, Kernel --- Algorism --- Learning, Machine --- AA / International- internationaal --- 303.5 --- 305.976 --- 305.971 --- Theorie van correlatie en regressie. (OLS, adjusted LS, weighted LS, restricted LS, GLS, SLS, LIML, FIML, maximum likelihood). Parametric and non-parametric methods and theory (wiskundige statistiek). --- Algoritmen. Optimisatie. --- Speciale gevallen in econometrische modelbouw. --- Planning (firm) --- Operational research. Game theory --- Geodesy --- Mathematical statistics --- Mathematics --- Probabilities --- Functions of complex variables --- Geometric function theory --- Algebra --- Arithmetic --- Artificial intelligence --- Machine theory --- Foundations --- Machine learning --- Algorithms --- Kernel functions --- Least squares --- E-books --- Support vector machines. --- Apprentissage automatique --- Algorithmes --- Noyaux (Mathématiques) --- Theorie van correlatie en regressie. (OLS, adjusted LS, weighted LS, restricted LS, GLS, SLS, LIML, FIML, maximum likelihood). Parametric and non-parametric methods and theory (wiskundige statistiek) --- Algoritmen. Optimisatie --- Speciale gevallen in econometrische modelbouw