Advances in Learning Theory: Methods, Models, and Applications

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IOS Press, 2003 - Computational learning theory - 415 pages
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Contents

An Overview of Statistical Learning Theory
1
Cucker Smale Learning Theory in Besov Spaces
47
Highdimensional Approximation by Neural Networks
69
Functional Learning through Kernels
89
Leaveoneout Error and Stability of Learning Algorithms with
111
Regularized LeastSquares Classification
131
Least Squares Approaches and Extensionsl55
154
Extension of the ISVM Range for Classification
179
Multiclass Learning with Output Codes
251
Bayesian Regression and Classification
267
from Likelihood Fields to Hyperfields
289
Bayesian Smoothing and Information Geometry
319
Nonparametric Prediction
341
Recent Advances in Statistical Learning Theory
357
Neural Networks in Measurement Systems an engineering view
375
Subject Index
411

Kernels Methods for Text Processing
197
An Optimization Perspective on Kernel Partial Least Squares Regres
227

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