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In the 1990s, a new type of learning algorithm was developed, based on results from statistical learning theory: the Support Vector Machine (SVM). This gave rise to a new class of theoretically elegant learning machines that use a central concept of SVMs -- -kernels--for a number of learning tasks. Kernel machines provide a modular framework that can be adapted to different tasks and domains by the choice of the kernel function and the base algorithm. They are replacing neural networks in a variety of fields, including engineering, information retrieval, and bioinformatics. Learning with Kernels provides an introduction to SVMs and related kernel methods. Although the book begins with the basics, it also includes the latest research. It provides all of the concepts necessary to enable a reader equipped with some basic mathematical knowledge to enter the world of machine learning using theoretically well-founded yet easy-to-use kernel algorithms and to understand and apply the powerful algorithms that have been developed over the last few years.

Complex analysis --- Computer assisted instruction --- Artificial intelligence. Robotics. Simulation. Graphics --- Machine learning --- Algorithms --- Kernel functions --- Apprentissage automatique --- Algorithmes --- Noyaux (Mathématiques) --- Machine Learning. --- Algorithms. --- Support vector machines. --- Kernel functions. --- Functions, Kernel --- SVMs (Algorithms) --- Noyaux (Mathématiques) --- Support vector machines --- Supervised learning (Machine learning) --- Functions of complex variables --- Geometric function theory --- E-books --- Machine learning. --- Algorism --- Algebra --- Arithmetic --- Learning, Machine --- Artificial intelligence --- Machine theory --- Foundations --- COMPUTER SCIENCE/Machine Learning & Neural Networks

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681.3*I2 --- 681.3*I2 Artificial intelligence. AI --- Artificial intelligence. AI --- Adaptive signal processing --- Algorithms --- Machine learning --- Learning, Machine --- Artificial intelligence --- Machine theory --- Algorism --- Algebra --- Arithmetic --- Signal processing, Adaptive --- Signal processing --- Foundations --- Machine Learning --- Adaptive signal processing. --- Algorithms. --- Machine learning. --- Traitement du signal

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Machine learning --- Neural networks (Computer science) --- Pattern perception --- Computer Science --- Engineering & Applied Sciences --- Computer science. --- Algorithms. --- Artificial intelligence. --- Image processing. --- Pattern recognition. --- Computer engineering. --- Computer Science. --- Computer Engineering. --- Artificial Intelligence (incl. Robotics). --- Pattern Recognition. --- Image Processing and Computer Vision. --- Algorithm Analysis and Problem Complexity. --- Computers --- Design perception --- Pattern recognition --- Form perception --- Perception --- Figure-ground perception --- Pictorial data processing --- Picture processing --- Processing, Image --- Imaging systems --- Optical data processing --- AI (Artificial intelligence) --- Artificial thinking --- Electronic brains --- Intellectronics --- Intelligence, Artificial --- Intelligent machines --- Machine intelligence --- Thinking, Artificial --- Bionics --- Cognitive science --- Digital computer simulation --- Electronic data processing --- Logic machines --- Machine theory --- Self-organizing systems --- Simulation methods --- Fifth generation computers --- Neural computers --- Algorism --- Algebra --- Arithmetic --- Informatics --- Science --- Design and construction --- Foundations --- Optical pattern recognition. --- Computer vision. --- Computer software. --- Artificial Intelligence. --- Software, Computer --- Computer systems --- Machine vision --- Vision, Computer --- Artificial intelligence --- Image processing --- Pattern recognition systems --- Perceptrons --- Visual discrimination --- Ensemble learning (Machine learning) --- Optical data processing. --- Optical computing --- Visual data processing --- Integrated optics --- Photonics --- Optical equipment --- Classifier combination (Machine learning) --- Committee-based learning (Machine learning) --- Ensemble methods (Machine learning) --- Multiple classifier systems (Machine learning) --- Distributed cognition --- Knowledge acquisition (Expert systems)

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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