Principles of Nonparametric Learning 

                        Udine (Italy), July 9-13, 2001

                        Advanced School coordinated by

                                  L. Györfi
                Budapest University of Technology and Economics
                                   Hungary


Modern nonparametric methods have become the most important tools in various
fields of applications of pattern recognition, density and regression function
estimation, data compression, on-line learning and prediction. The common
feature in these problems is that some unknown underlying system generates data
and the best action is to be learnt from these data.

The purpose of the course is to teach the basic principles of nonparametric
inference with emphasis on the cited areas. Leading international experts of
these areas will introduce the participants into the theory and advanced methods
of inference.

A prototype problem to be discussed in depth is the problem of pattern
recognition in which an observation is to be classified into one of a finite
number of classes. The optimal classification must be approximated based on
training data. Classical nonparametric methods such as nearest neighbor and
kernel methods, as well as modern methodologies including neural networks,
support vector machines, and binary tree classifiers will be studied in the
course. Evolutionary optimization methodologies, e.g. genetic programming,
will also be considered as they allow nonparametric learning to handle complex
data and explore structured model spaces.

The methodologies covered by this course have found applications in various
fields such as identification of biological and mechanical systems, data mining,
forecasting, universal data compression, optimal portfolio strategies for stock
markets, and measurement-based call admission control for high-speed communication
networks.  

The course is addressed to postgraduates in engineering, mathematics, and computer
science, and researchers in universities and research institutions.

Please visit the school home page (www.cism.it/c2001/c03/) for information
on program, admission, and accomodation.