Contents

• Preface
• Organizing committee
• List of chapter contributors
• Table of contents [ pdf, ps ]
  
  
• An Overview of Statistical Learning Theory
  V. Vapnik
  
  
• Best Choices for Regularization Parameters in Learning Theory: on the Bias-Variance Problem
  F. Cucker, S. Smale
  
  
• Cucker Smale Learning Theory in Besov Spaces
  C.A. Micchelli, Y. Xu, P. Ye
  
  
• High-dimensional Approximation by Neural Networks
  V. Kurkova
  
  
• Functional Learning through Kernels
  S. Canu, X. Mary, A. Rakotomamonjy
  
  
• Leave-one-out Error and Stability of Learning Algorithms with Applications
  A. Elisseeff, M. Pontil
  
  
• Regularized Least-Squares Classification
  R. Rifkin, G. Yeo, T. Poggio
  
  
• Support Vector Machines: Least Squares Approaches and Extensions
  J.A.K. Suykens, T. Van Gestel, J. De Brabanter, B. De Moor, J. Vandewalle
  
  
• Extension of the nu-SVM Range for Classification
  F. Perez-Cruz, J. Weston, D.J.L. Herrmann, B. Schoelkopf
  
  
• Kernels Methods for Text Processing
  N. Cristianini, J. Kandola, A. Vinokourov, J. Shawe-Taylor
  
  
• An Optimization Perspective on Kernel Partial Least Squares Regression
  K.P. Bennett, M.J. Embrechts
  
  
• Multiclass Learning with Output Codes
  Y. Singer
  
  
• Bayesian Regression and Classification
  C.M. Bishop, M.E. Tipping
  
  
• Bayesian Field Theory: from Likelihood Fields to Hyperfields
  J. Lemm
  
  
• Bayesian Smoothing and Information Geometry
  R. Kulhavy
  
  
• Nonparametric Prediction
  L. Gyorfi, D. Schafer
  
  
• Recent Advances in Statistical Learning Theory
  M. Vidyasagar
  
  
• Neural Networks in Measurement Systems (an engineering view)
  G. Horvath
  
  
• List of participants
• Subject index
• Author index