Learning From Data: Concepts, Theory, and Methods
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More About This Title Learning From Data: Concepts, Theory, and Methods
English
VLADIMIR CHERKASSKY is on the faculty of electrical and computer engineering at the University of Minnesota. His current research is on neural network and statistical methods for estimating dependencies from data. Professor Cherkassky is on the governing board of the International Neural Network Society (INNS). He was an organizer of the NATO Advanced Study Institute symposium, From Statistics to Neural Networks, held in France in 1993. FILIP MULIER received a PhD in electrical engineering from the University of Minnesota in 1994. He currently works with a large multinational corporation on industrial applications of learning methods. His current research is on practical applications of learning theory, including industrial process control and financial market prediction.
English
Problem Statement, Classical Approaches, and Adaptive Learning.
Regularization Framework.
Statistical Learning Theory.
Nonlinear Optimization Strategies.
Methods for Data Reduction and Dimensionality Reduction.
Methods for Regression.
Classification.
Support Vector Machines.
Fuzzy Systems.
Appendices.
Index.
Regularization Framework.
Statistical Learning Theory.
Nonlinear Optimization Strategies.
Methods for Data Reduction and Dimensionality Reduction.
Methods for Regression.
Classification.
Support Vector Machines.
Fuzzy Systems.
Appendices.
Index.
English
"This book contains considerable information on the concept of statistical learning theory.... However, some may find its presentation difficult to follow..." (Technometrics, February, 2001)
"...well readable..." (Zentralblatt Math, Vol.960, No.10 2001)
"...well readable..." (Zentralblatt Math, Vol.960, No.10 2001)
