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» The Shortcut Problem - Complexity and Approximation
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IEEEICCI
2006
IEEE
15 years 10 months ago
Rough Set Method Based on Multi-Granulations
The original rough set model is concerned primarily with the approximation of sets described by single binary relation on universe. In the view of granular computing, classical ro...
Y. H. Qian, J. Y. Liang
IJCNN
2006
IEEE
15 years 10 months ago
Cellular SRN Trained by Extended Kalman Filter Shows Promise for ADP
— Cellular simultaneous recurrent neural network has been suggested to be a function approximator more powerful than the MLP’s, in particular for solving approximate dynamic pr...
Roman Ilin, Robert Kozma, Paul J. Werbos
ICDT
2001
ACM
153views Database» more  ICDT 2001»
15 years 9 months ago
Estimating Range Queries Using Aggregate Data with Integrity Constraints: A Probabilistic Approach
In fast OLAP applications it is often advantageous to provide approximate answers to range queries in order to achieve very high performances. A possible solution is to inquire sum...
Francesco Buccafurri, Filippo Furfaro, Domenico Sa...
UAI
2008
15 years 5 months ago
Bounds on the Bethe Free Energy for Gaussian Networks
We address the problem of computing approximate marginals in Gaussian probabilistic models by using mean field and fractional Bethe approximations. As an extension of Welling and ...
Botond Cseke, Tom Heskes
NC
1998
101views Neural Networks» more  NC 1998»
15 years 5 months ago
Evolutionary Optimized Tensor Product Bernstein Polynomials versus Backpropagation Networks
In this paper a new approach for approximation problems involving only few input and output parameters is presented and compared to traditional Backpropagation Neural Networks (BP...
Günther R. Raidl, Gabriele Kodydek