**BOOK DESCRIPTION**
The book provides systematic in-depth analysis of nonparametric learning.
It covers the theoretical limits and the asymptotical optimal algorithms
and estimates, such as pattern recognition, nonparametric regression estimation,
universal prediction, vector quantization, distribution and density estimation
and genetic programming. The book is mainly addressed to postgraduates
in engineering, mathematics, computer science, and researchers in universities
and research institutions.

Keywords: Signal Processing, Statistical Theory and Methods, Probability
and Statistics in Computer Science, Pattern Recognition

**Contents:**

**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

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