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Scattered Data Approximation

, and partitions of unity. The book starts with an overview on typical applications of scattered data approximation, coming from surface;

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Scattered Data Approximation

, and partitions of unity. The book starts with an overview on typical applications of scattered data approximation, coming from surface;

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Curves and Surfaces

, geometry processing, image and signal processing, interpolation and smoothing, mesh generation, finite elements and splines, scattered data;

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Meshfree Approximation Methods With Matlab (With Cd-rom)

scattered data approximation and partial differential equations point of view. A good balance is supplied between the necessary theory and;

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Multiresolution Methods in Scattered Data Modelling

phenomena. To this end, various modern techniques from scattered data modelling, such as splines over triangulations and radial basis functions, are;

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Approximation Theory and Algorithms for Data Analysis

algorithms for data analysis plays an important role. The following topics are covered: * least-squares approximation and regularization methods;

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Soft Computing In Systems And Control Technology

, neurofuzzy and genetic methodologies are: data fusion, reinforcement learning, approximation properties, multichannel imaging, signal processing;

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Foundations of Computational Intelligence Volume 5

Foundations of Computational Intelligence Volume 5: Function Approximation and Classification Approximation theory is that area of analysis;

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Approximation Techniques for Engineers

major techniques used in modern engineering practice. Whether you need approximations for discrete data of continuous functions, or you're;

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Introduction to Imaging from Scattered Fields

Obtain the Best Estimate of a Strongly Scattering Object from Limited Scattered Field Data Introduction to Imaging from Scattered Fields;

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Mathematical Programming with Data Perturbations

Presents research contributions and tutorial expositions on current methodologies for sensitivity, stability and approximation analyses;

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Cambridge Tracts in Mathematics

Stochastic approximation is a relatively new technique for studying the properties of an experimental situation; it has important;

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Data Analysis and Approximate Models

treats models as approximations to data, not to some underlying truth. The author develops a concept of approximation for probability models;

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Greedy Approximation

in image/signaldata processing, as well as in the design of neural networks. The fundamental question of nonlinear approximation is how to devise;

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Introduction to Imaging from Scattered Fields

Obtain the Best Estimate of a Strongly Scattering Object from Limited Scattered Field Data Introduction to Imaging from Scattered;

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Deep Neural Networks

WASD neuronet models, three-input WASD neuronet models, and general multi-input WASD neuronet models for function data approximations;

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Deep Neural Networks

WASD neuronet models, three-input WASD neuronet models, and general multi-input WASD neuronet models for function data approximations;

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Mathematical Programming with Data Perturbations

Presents research contributions and tutorial expositions on current methodologies for sensitivity, stability and approximation analyses;

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The Best Approximation Method in Computational Mechanics

, despite the vast quantities of synthetic data there is still some doubt concerning the validity and accuracy of these approximations. This;

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Fundamentals of Data Analytics

This book introduces the basic methodologies for successful data analytics. Matrix optimization and approximation are explained in detail;

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Fundamentals of Data Analytics

This book introduces the basic methodologies for successful data analytics. Matrix optimization and approximation are explained in detail;

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Approximation and Modeling with B-Splines

B-splines are fundamental to approximation and data fitting, geometric modeling, automated manufacturing, computer graphics, and numerical;

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Low Rank Approximation

approximation;* missing data estimation;* data-driven filtering and control;* stochastic model representation and identification;* identification;

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Ridge Functions

of large multivariate data sets, the MLP model in neural networks, Waring's problem over linear forms, and approximation theory. Ridge Functions;

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Foundations of Applied Mathematics, Volume 2

of algorithms for mathematicians and data science students; probability and its applications; the theory and applications of approximation, including;

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Approximation Theory

Contains the contributions of 45 internationally distinguished mathematicians covering all areas of approximation theory-written in honor;

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Data Warehouse ETL Toolkit

) process Delineates best practices for extracting data from scattered sources, removing redundant and inaccurate data, transforming the remaining;

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