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» Optimizing efficiency of perturbative Monte Carlo method
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PRL
2000
182views more  PRL 2000»
14 years 9 months ago
Bayesian MLP neural networks for image analysis
We demonstrate the advantages of using Bayesian multi layer perceptron (MLP) neural networks for image analysis. The Bayesian approach provides consistent way to do inference by c...
Aki Vehtari, Jouko Lampinen
STOC
1998
ACM
135views Algorithms» more  STOC 1998»
15 years 1 months ago
Checking Polynomial Identities over any Field: Towards a Derandomization?
We present a Monte Carlo algorithm for testing multivariate polynomial identities over any field using fewer random bits than other methods. To test if a polynomial P(x1 ::: xn) ...
Daniel Lewin, Salil P. Vadhan
PRL
2006
139views more  PRL 2006»
14 years 9 months ago
Adaptive Hausdorff distances and dynamic clustering of symbolic interval data
This paper presents a partitional dynamic clustering method for interval data based on adaptive Hausdorff distances. Dynamic clustering algorithms are iterative two-step relocatio...
Francisco de A. T. de Carvalho, Renata M. C. R. de...
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GECCO
2005
Springer
111views Optimization» more  GECCO 2005»
15 years 3 months ago
XCS with eligibility traces
The development of the XCS Learning Classifier System has produced a robust and stable implementation that performs competitively in direct-reward environments. Although investig...
Jan Drugowitsch, Alwyn Barry
FOCM
2010
140views more  FOCM 2010»
14 years 8 months ago
Combinatorial Sublinear-Time Fourier Algorithms
We study the problem of estimating the best k term Fourier representation for a given frequency-sparse signal (i.e., vector) A of length N k. More explicitly, we investigate how t...
Mark A. Iwen