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CRV
2009
IEEE
115views Robotics» more  CRV 2009»
15 years 4 months ago
Learning Model Complexity in an Online Environment
In this paper we introduce the concept and method for adaptively tuning the model complexity in an online manner as more examples become available. Challenging classification pro...
Dan Levi, Shimon Ullman
ANSS
1996
IEEE
15 years 1 months ago
Computation of the Asymptotic Bias and Variance for Simulation of Markov Reward Models
The asymptotic bias and variance are important determinants of the quality of a simulation run. In particular, the asymptotic bias can be used to approximate the bias introduced b...
Aad P. A. van Moorsel, Latha A. Kant, William H. S...
BMCBI
2006
150views more  BMCBI 2006»
14 years 9 months ago
Predicting protein subcellular locations using hierarchical ensemble of Bayesian classifiers based on Markov chains
Background: The subcellular location of a protein is closely related to its function. It would be worthwhile to develop a method to predict the subcellular location for a given pr...
Alla Bulashevska, Roland Eils
CORR
2012
Springer
170views Education» more  CORR 2012»
13 years 5 months ago
What Cannot be Learned with Bethe Approximations
We address the problem of learning the parameters in graphical models when inference is intractable. A common strategy in this case is to replace the partition function with its B...
Uri Heinemann, Amir Globerson
TACAS
2010
Springer
210views Algorithms» more  TACAS 2010»
15 years 4 months ago
Automatic Analysis of Scratch-Pad Memory Code for Heterogeneous Multicore Processors
Modern multicore processors, such as the Cell Broadband Engine, achieve high performance by equipping accelerator cores with small “scratchpad” memories. The price for increase...
Alastair F. Donaldson, Daniel Kroening, Philipp R&...