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Kernel-based Approximation Methods Using Matlab

functions, this book presents modern theoretical results on kernel-based approximation methods and demonstrates their implementation in a variety;

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Approximation Theory and Algorithms for Data Analysis

* asymptotic concepts: error estimates and convergence rates * signal approximation by Fourier and wavelet methods * kernel-based multivariate;

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Meshfree Approximation Methods With Matlab (With Cd-rom)

needed for a basic understanding of meshfree approximation methods.The emphasis here is on a hands-on approach that includes MATLAB routines for;

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Computational Techniques for Process Simulation and Analysis Using MATLAB (R)

examples. * Focus on simulation using MATLAB to solve ODEs and PDEs that are frequently encountered in process systems.;

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Computational Techniques for Process Simulation and Analysis Using MATLAB (R)

examples. * Focus on simulation using MATLAB to solve ODEs and PDEs that are frequently encountered in process systems.;

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Kernel Smoothing in MATLAB

is an integral part of the book. This toolbox contains many Matlab scripts useful for kernel smoothing of density, cumulative distribution;

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Radial Basis Function (RBF) Neural Network Control for Mechanical Systems

stable adaptive control system design using neural network approximation-based techniques. The main objectives of the book are to introduce the;

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Kernel Methods And Machine Learning

Offering a fundamental basis in kernel-based learning theory, this book covers both statistical and algebraic principles. It provides;

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Digital Signal Processing with Kernel Methods

communications, multimedia, and biomedical engineering systems Digital Signal Processing with Kernel Methods reviews the milestones in the mixing;

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Linear Programming with MATLAB

This textbook provides a self-contained introduction to linear programming using MATLAB(R) software to elucidate the development;

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Kernel Learning Algorithms for Face Recognition

Kernel Learning Algorithms for Face Recognition covers the framework of kernel based face recognition. This book discusses the advanced;

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Kernel Methods for Remote Sensing Data Analysis

present state-of-the-art research related to remote sensing based on the recent advances in kernel methods, analysing the related methodological;

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Numerical Methods for Engineers and Scientists Using MATLAB (R)

This book provides a pragmatic, methodical and easy-to-follow presentation of numerical methods and their effective implementation using;

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Generalized Mercer Kernels and Reproducing Kernel Banach Spaces

This article studies constructions of reproducing kernel Banach spaces (RKBSs) which may be viewed as a generalization of reproducing;

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Practical Numerical and Scientific Computing with MATLAB (R) and Python

linear and non-linear programming. It discusses the methods for solving different types of mathematical problems using MATLAB and Python;

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Practical Numerical and Scientific Computing with MATLAB (R) and Python

linear and non-linear programming. It discusses the methods for solving different types of mathematical problems using MATLAB and Python;

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MATLAB

integration methods, engineering mechanics and mechanical vibrations are covered using MATLAB software. The numerous worked examples and unsolved;

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Investment Strategies Optimization based on a SAX-GA Methodology

kernel based on genetic algorithms (GA). While the SAX representation is used to describe the financial time series, the evolutionary;

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Numerical Mathematics

-life applications. This is done using the MATLAB software environment, which allows an easy implementation and testing of the algorithms for;

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Introduction to Finite Element Analysis Using MATLAB(R) and Abaqus

Finite Element Analysis Using MATLAB (R) and Abaqus accomplishes both. This book teaches the first principles of the finite element method. It;

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Unsupervised Process Monitoring and Fault Diagnosis with Machine Learning Methods

examines such cutting-edge topics as the use of information theory to enhance unsupervised learning in tree-based methods, the extension of kernel;

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Tensor Numerical Methods in Scientific Computing

algorithms for the sinc-based separable approximation of the analytic radial basis functions including Green's and Helmholtz kernels. The efficient;

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Model Reduction of Complex Dynamical Systems

approximation, and reduced-basis methods; data-driven methods, including Loewner matrix and pencil-based approaches, dynamic mode decomposition, and;

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Uncertainty Quantification and Stochastic Modeling with Matlab

Quantification using Functional AnalysisDetails listings of Matlab programs implementing the main methods which complete the methodological;

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Kernel Adaptive Filtering

Reproducing kernel Hilbert spaces is a topic of great current interest for applications in signal processing, communications, and controls;

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Weighted Polynomial Approximation and Numerical Methods for Integral Equations

The book presents a combination of two topics: one coming from the theory of approximation of functions and integrals by interpolation and;

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Weighted Polynomial Approximation and Numerical Methods for Integral Equations

The book presents a combination of two topics: one coming from the theory of approximation of functions and integrals by interpolation and;

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