Computer-Intensive Methods in Control and Signal Processing
The Curse of Dimensionality
Birkhäuser Verlag GmbH
Published in April 1997
Book
Hardback
320 pages
978-3-7643-3989-0 (ISBN)
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Description
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.
More details
Language
English
Place of publication
Basel
Switzerland
Target group
College/higher education
Professional and scholarly
Illustrations
60 Abb.
Dimensions
Height: 24 cm
Width: 16 cm
ISBN-13
978-3-7643-3989-0 (9783764339890)
Schweitzer Classification
Other editions
New editions

Kevin Warwick | Miroslav Karny
Computer Intensive Methods in Control and Signal Processing
The Curse of Dimensionality
Book
03/1997
Birkhauser Boston Inc
€106.99
Shipment within 15-20 days
Content
Fighting dimensionality with linguistic geometry, Boris Stilman; Statistical physics and the optimization of autonomous behaviour in complex virtual worlds, Robert W. Penney; On merging gradient estimation with mean-tracking techniques for cluster identification, Paul D. Fox et al; Computational aspects of graph theoretic methods in control, Katalin M. Hangos, Zsolt Tuza; Efficient algorithms for predictive control of systems with bounded inputs, Luigi Chisci et al; Applying new numerical algorithms to the solution of discrete-time optimal control problems, Rudiger Franke, Eckhard Arnold; System identification using composition networks, Yves Moreau, Joos Vandewalle; Recursive nonlinear estimation of non-linear/non-Gaussian dynamic models, Rudolf Kulhavy; Monte Carlo approach to Bayesian regression modelling, Jan Smid et al; Identification of reality in Bayesian context, Ludek Berec, Miroslav Karny; Nonlinear nonnormal dynamic models - state estimation and software, Miroslav Simandl, Miroslav Flidr; The EM algorithm - a guided tour, Christophe Couvreur; estimation of quasipolynomilas in noise - theoretical algorithmic and implementation aspects, Vytautas Slivinskas, Virginija Simonyte; Iterative reconstruction of transmission sinograms with low signal to noise ratio, Johan Nuyts et al; Curse of dimensionality - classifying large multi-dimensional images with neural networks, Rudolf Hanka, Thomas P. Harte; Dimension-independent rates of approximation by neural networks, Vera Kurkova; estimation of human signal detection performance from event-related potentials using feed-forward neural network model, Milos Koska et al; Utilizing geometric anomalies of high dimension - when complexity makes computation easier, Paul C. Kainen; Approximation using cubic B-splines with improved training speed and accuracy, Julian D. Mason et al.