The P-GRADE job execution mode will be demonstrated on a small Grid containing 3 clusters from Budapest and London. The first demonstration illustrates the Grid execution of a parallel meteorology application. The par...
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Differential linear repetitive processes are a class of continuous-discrete 2D linear systems of both systems theoretic and applications interest. The feature which makes them distinct from other classes of such syste...
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Differential linear repetitive processes are a class of continuous-discrete 2D linear systems of both systems theoretic and applications interest. The feature which makes them distinct from other classes of such systems is that information propagation in one of the two independent directions only occurs over a finite interval. Applications areas include iterative learning control and iterative solution algorithms for classes of dynamic nonlinear optimal control problems based on the maximum principle. In this paper, we investigate further the structural links between differential linear repetitive processes and a special class of time delay systems. This leads to some significant new controllability and optimal control results for these processes.
We extend the classical algorithms of Valiant and Haussler for learning compact conjunctions and disjunctions of Boolean attributes to allow features that are constructed from the data and to allow a trade-off between...
We extend the classical algorithms of Valiant and Haussler for learning compact conjunctions and disjunctions of Boolean attributes to allow features that are constructed from the data and to allow a trade-off between accuracy and complexity. The result is a general-purpose learning machine, suitable for practical learning tasks, that we call the set covering machine. We present a version of the set covering machine that uses data-dependent balls for its set of features and compare its performance with the support vector machine. By extending a technique pioneered by Littlestone and Warmuth, we bound its generalization error as a function of the amount of data compression it achieves during training. In experiments with real-world learning tasks, the bound is shown to be extremely tight and to provide an effective guide for model selection.
Multiresolution representation of quadrilateral surface approximation (MRQSA) is a useful representation for progressive graphics transmission in networks. Based on two requirements: (1) minimum mean square error and ...
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Recently, multiple classifier systems (MCS) have been used for practical applications to improve classification accuracy. Self-generating neural networks (SGNN) are one of the suitable base classifiers for MCS because...
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