Computer Intensive Methods in Control and Signal Processing

The Curse of Dimensionality

Inbunden, Engelska, 1997

AvKevin Warwick,Miroslav Karny

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This volume is based upon the 2nd IEEE European Workshop on Computer-Intensive Methods in Control and Signal Processing, subtitled "Can We Beat the Curse of Dimensionality?", held in Prague, August 1996. The book brings together a blend of approaches to a common theme from a variety of international research groups in engineering. The 18 contributions which make up this text originated as selected papers from the 1996 workshop, and have been modified and edited to appear here. The range of engineering fields addressed include: multi-agent systems; signal processing; pattern recognition; expert systems; nonparametric estimation; and artificial neural networks.

Produktinformation

Utgivningsdatum1997-03-01
Mått155 x 235 x 23 mm
Vikt653 g
FormatInbunden
SpråkEngelska
Antal sidor303
Upplaga1997
FörlagBirkhauser Boston Inc
ISBN9780817639891

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1. Fighting Dimensionality with Linguistic Geometry.- 2. Statistical Physics and the Optimization of Autonomous Behaviour in Complex Virtual Worlds.- 3. On Merging Gradient Estimation with Mean-Tracking Techniques for Cluster Identification.- 4. Computational Aspects of Graph Theoretic Methods in Control.- 5. Efficient Algorithms for Predictive Control of Systems with Bounded Inputs.- 6. Applying New Numerical Algorithms to the Solution of Discrete-time Optimal Control Problems.- 7. System Identification using Composition Networks.- 8. Recursive Nonlinear Estimation of Non-linear/Non-Gaussian Dynamic Models.- 9. Monte Carlo Approach to Bayesian Regression Modelling.- 10. Identification of Reality in Bayesian Context.- 11. Nonlinear Nonnormal Dynamic Models: State Estimation and Software.- 12. The EM Algorithm: A Guided Tour.- 13. Estimation of Quasipolynomials in Noise: Theoretical, Algorithmic and Implementation Aspects.- 14. Iterative Reconstruction of Transmission Sinograms with Low Signal to Noise Ratio.- 15. Curse of Dimensionality: Classifying Large Multi-Dimensional Images with Neural Networks.- 16. Dimension-independent Rates of Approximation by Neural Networks.- 17. Estimation of Human Signal Detection Performance from Event-Related Potentials Using Feed-Forward Neural Network Model.- 18. Utilizing Geometric Anomalies of High Dimension: When Complexity Makes Computation Easier.- 19. Approximation Using Cubic B-Splines with Improved Training Speed and Accuracy.