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ML
2007
ACM
192views Machine Learning» more  ML 2007»
15 years 6 months ago
Annealing stochastic approximation Monte Carlo algorithm for neural network training
We propose a general-purpose stochastic optimization algorithm, the so-called annealing stochastic approximation Monte Carlo (ASAMC) algorithm, for neural network training. ASAMC c...
Faming Liang
ECCV
2008
Springer
16 years 8 months ago
Solving Image Registration Problems Using Interior Point Methods
Abstract. This paper describes a novel approach to recovering a parametric deformation that optimally registers one image to another. The method proceeds by constructing a global c...
Camillo J. Taylor, Arvind Bhusnurmath
NIPS
2007
15 years 8 months ago
Random Sampling of States in Dynamic Programming
We combine three threads of research on approximate dynamic programming: sparse random sampling of states, value function and policy approximation using local models, and using lo...
Christopher G. Atkeson, Benjamin Stephens
DEDS
2010
97views more  DEDS 2010»
15 years 6 months ago
On Regression-Based Stopping Times
We study approaches that fit a linear combination of basis functions to the continuation value function of an optimal stopping problem and then employ a greedy policy based on the...
Benjamin Van Roy
DFG
2007
Springer
16 years 28 days ago
Natural Neighbor Concepts in Scattered Data Interpolation and Discrete Function Approximation
: The concept of natural neighbors employs the notion of distance to define local neighborhoods in discrete data. Especially when querying and accessing large scale data, it is im...
Tom Bobach, Georg Umlauf