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» Approximate algorithms for neural-Bayesian approaches
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SIAMCO
2000
71views more  SIAMCO 2000»
14 years 9 months ago
On a Perturbation Approach for the Analysis of Stochastic Tracking Algorithms
In this paper, a perturbation expansion technique is introduced to decompose the tracking error of a general adaptive tracking algorithm in a linear regression model. This method ...
Rafik Aguech, Eric Moulines, Pierre Priouret
ICML
2010
IEEE
14 years 11 months ago
Accelerated dual decomposition for MAP inference
Approximate MAP inference in graphical models is an important and challenging problem for many domains including computer vision, computational biology and natural language unders...
Vladimir Jojic, Stephen Gould, Daphne Koller
KSEM
2009
Springer
15 years 4 months ago
A Competitive Learning Approach to Instance Selection for Support Vector Machines
Abstract. Support Vector Machines (SVM) have been applied successfully in a wide variety of fields in the last decade. The SVM problem is formulated as a convex objective function...
Mario Zechner, Michael Granitzer
SIAMMA
2011
97views more  SIAMMA 2011»
14 years 20 days ago
Convergence Rates for Greedy Algorithms in Reduced Basis Methods
The reduced basis method was introduced for the accurate online evaluation of solutions to a parameter dependent family of elliptic partial differential equations. ly, it can be ...
Peter Binev, Albert Cohen, Wolfgang Dahmen, Ronald...
ICRA
2008
IEEE
123views Robotics» more  ICRA 2008»
15 years 4 months ago
Exact state and covariance sub-matrix recovery for submap based sparse EIF SLAM algorithm
— This paper provides a novel state vector and covariance sub-matrix recovery algorithm for a recently developed submap based exactly sparse Extended Information Filter (EIF) SLA...
Shoudong Huang, Zhan Wang, Gamini Dissanayake