
Learning Theory by Felipe Cucker
The goal of learning theory is to approximate a function from sample values. To attain this goal learning theory draws on a variety of diverse subjects, specifically statistics, approximation theory, and algorithmics. Ideas from all these areas blended to form a subject whose many successful applications have triggered a rapid growth during the last two decades. This is the first book to give a general overview of the theoretical foundations of the subject emphasizing the approximation theory, while still giving a balanced overview. It is based on courses taught by the authors, and is reasonably self-contained so will appeal to a broad spectrum of researchers in learning theory and adjacent fields. It will also serve as an introduction for graduate students and others entering the field, who wish to see how the problems raised in learning theory relate to other disciplines.-
Operator-Adapted Wavelets, Fast Solvers, and Numerical Homogenization
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Kernel-Based Methods
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Infinite-Dimensional Spectral Computations
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Numerical Bifurcation Analysis of Maps
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Collocation Methods for Volterra Integral and Related Functional Differential Equations
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The Mathematical Foundations of Mixing
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Partial Differential Equation Methods for Image Inpainting
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Topology for Computing
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High-Order Methods for Incompressible Fluid Flow
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Generalized Riemann Problems in Computational Fluid Dynamics
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Multivariate Approximation
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Iterative Krylov Methods for Large Linear Systems
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Dynamical Systems and Numerical Analysis
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Volterra Integral Equations
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Scattered Data Approximation
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Greedy Approximation
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A Practical Guide to the Invariant Calculus
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Orthogonal Rational Functions
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Geometry and Topology for Mesh Generation
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Curve and Surface Reconstruction
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Algebraic Geometry and Statistical Learning Theory
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The Numerical Solution of Integral Equations of the Second Kind
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Radial Basis Functions
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A Practical Guide to Pseudospectral Methods
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Spectral Methods for Time-Dependent Problems
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Multiscale Methods for Fredholm Integral Equations
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Differential Equations and Variational Methods on Graphs
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Minimal Cubature Rules
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High-Accuracy Finite Difference Methods
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An Introduction to Immersed Boundary Methods
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Modern Computer Arithmetic
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Matrix Preconditioning Techniques and Applications
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Geometry of the Phase Retrieval Problem
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Quasi-Interpolation
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The Christoffel–Darboux Kernel for Data Analysis
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Difference Equations by Differential Equation Methods
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Schwarz-Christoffel Mapping
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Simulating Hamiltonian Dynamics
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Spectral and Spectral Element Methods for Fractional Ordinary and Partial Differential Equations
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Discrete Variational Problems with Interfaces
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Spaces of Measures and their Applications to Structured Population Models
'The book is well suited for its target audience, which includes researchers and graduate studentsThey will, no doubt, be reassured to find that each chapter closes with a collection of references and additional remarks, which place the preceding information in a wider context. … Overall, this text is another excellent addition to the Applied and Computational Mathematics series published by Cambridge University Press. It complements other titles in the series without duplicating material and should be of value to anyone interested in learning theory or a neighbouring field.' Mathematics Today
'… the book under review focuses on the mathematical foundations of learning theory. It is an excellent monograph on the subject. A major novelty is the focus on the point of view of approximation. This distinguishes the book from the majority of previous works on learning theory, which share a prevalent statistics/computer science flavor. However, this doesn't mean at all that the monograph is written only for 'approximation people'. On the contrary, it nicely provides a general overview of the theoretical foundations of the subject also to a broad spectrum of researchers in learning and related fields.' Mathematical Reviews
'… the book under review focuses on the mathematical foundations of learning theory. It is an excellent monograph on the subject. A major novelty is the focus on the point of view of approximation. This distinguishes the book from the majority of previous works on learning theory, which share a prevalent statistics/computer science flavor. However, this doesn't mean at all that the monograph is written only for 'approximation people'. On the contrary, it nicely provides a general overview of the theoretical foundations of the subject also to a broad spectrum of researchers in learning and related fields.' Mathematical Reviews
Felipe Cucker is a Professor of Mathematics at the City University of Hong Kong. Ding Xuan Zhou is an Associate Professor in the Department of Mathematics at the City University of Hong Kong.
| SKU | Unavailable |
| ISBN 13 | 9780521865593 |
| ISBN 10 | 052186559X |
| Title | Learning Theory |
| Author | Felipe Cucker |
| Series | Cambridge Monographs On Applied And Computational Mathematics |
| Condition | Unavailable |
| Binding Type | Hardback |
| Publisher | Cambridge University Press |
| Year published | 2007-03-29 |
| Number of pages | 238 |
| Cover note | Book picture is for illustrative purposes only, actual binding, cover or edition may vary. |
| Note | Unavailable |








































