04233nmm a2200397 u 4500001001200000003002700012005001700039007002400056008004100080020001800121100001900139245015200158250001700310260004000327300003200367505151500399653002301914653003401937653003101971653003102002653003102033653004402064653004002108653002402148653001302172653002202185653001502207700003002222041001902252989003802271028003002309856007202339082001302411082000802424520140302432EB000618876EBX0100000000000000047195800000000000000.0cr|||||||||||||||||||||140122 ||| eng a97814612199651 aWarwick, Kevin00aComputer Intensive Methods in Control and Signal ProcessinghElektronische RessourcebThe Curse of Dimensionalitycby Kevin Warwick, Miroslav Karny a1st ed. 1997 aBoston, MAbBirkhäuserc1997, 1997 aXVI, 303 pbonline resource0 a1. 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 aMicroprogramming aControl, Robotics, Automation aComputational intelligence aControl and Systems Theory aComputational Intelligence aControl Structures and Microprogramming aSignal, Speech and Image Processing aControl engineering aRobotics aSignal processing aAutomation1 aKarny, Miroslave[author]07aeng2ISO 639-2 bSBAaSpringer Book Archives -200450a10.1007/978-1-4612-1996-540uhttps://doi.org/10.1007/978-1-4612-1996-5?nosfx=yxVerlag3Volltext0 a629.83120 a003 aDue to the rapid increase in readily available computing power, a corre sponding increase in the complexity of problems being tackled has occurred in the field of systems as a whole. A plethora of new methods which can be used on the problems has also arisen with a constant desire to deal with more and more difficult applications. Unfortunately by increasing the ac curacy in models employed along with the use of appropriate algorithms with related features, the resultant necessary computations can often be of very high dimension. This brings with it a whole new breed of problem which has come to be known as "The Curse of Dimensionality" . The expression "Curse of Dimensionality" can be in fact traced back to Richard Bellman in the 1960's. However, it is only in the last few years that it has taken on a widespread practical significance although the term di mensionality does not have a unique precise meaning and is being used in a slightly different way in the context of algorithmic and stochastic complex ity theory or in every day engineering. In principle the dimensionality of a problem depends on three factors: on the engineering system (subject), on the concrete task to be solved and on the available resources. A system is of high dimension if it contains a lot of elements/variables and/or the rela tionship/connection between the elements/variables is complicated