Principles of Nonparametric Learning

by László Györfi (editor)

Pattern classification and learning theory (G. Lugosi): A binary classification problem; Empirical risk minimization; Concentration inequalities; Vapnik-Chervonenkis theory; Minimax lower bounds; Complexity regularization; References.- Nonparametric regression estimation (L. Györfi, M. Kohler): Regression problem; Local averaging estimates; Consequences in pattern recognition; Definition of (penalized) least squares estimates; Consistency of least squares estimates; Consistency of penalized least squares estimates; Rate of convergence of least squares estimates; References.- Universal prediction (N. Cesa-Bianchi): Introduction; Potential-based forecasters; Convex loss functions; Exp-concave loss functions; Absolute loss; Logarithmic loss; Sequentioal pattern classification; References.- Learning-theoretic methods in vector quantization (T. Linder): Introduction; The fixed-rate quantization problem; Consistency of empirical design; Finite sample upper bounds; Minimax lower bounds; Fundamentals of variable-rate quantization; The Lagrangian formulation; Consistency of Lagrangian empirical design; Finite sample bounds in Lagrangian design; References.- Distribution and density estimation (L. Devroye, L. Györfi): Distribution estimation; The density estimation problem; The histogram density estimate; Choosing Between Two Densities; The Minimum Distance Estimate; The Kernel Density Estimate; Additive Estimates and Data Splitting; Bandwidth Selection for Kernel Estimates; References.- Programming applied to model identification (M. Sebag): Summary; Introduction; Artificial Evolution; Genetic Programming; Genetic Programming with Grammars; Discussion and Conclusion; References

348 pages, Paperback

First published September 10, 2002