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ICIP
2005
IEEE
16 years 1 months ago
Hexagonal versus orthogonal lattices: a new comparison using approximation theory
We provide a new comparison between hexagonal and orthogonal lattices, based on approximation theory. For each of the lattices, we select the "natural" spline basis func...
Laurent Condat, Dimitri Van De Ville, Thierry Blu
CP
2006
Springer
15 years 3 months ago
Approximability of Integer Programming with Generalised Constraints
We study a family of problems, called Maximum Solution, where the objective is to maximise a linear goal function over the feasible integer assignments to a set of variables subjec...
Peter Jonsson, Fredrik Kuivinen, Gustav Nordh
ML
2007
ACM
192views Machine Learning» more  ML 2007»
14 years 11 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
TOMACS
2010
79views more  TOMACS 2010»
14 years 6 months ago
A stochastic approximation method with max-norm projections and its applications to the Q-learning algorithm
In this paper, we develop a stochastic approximation method to solve a monotone estimation problem and use this method to enhance the empirical performance of the Q-learning algor...
Sumit Kunnumkal, Huseyin Topaloglu
CPM
2000
Springer
124views Combinatorics» more  CPM 2000»
15 years 4 months ago
Indexing Text with Approximate q-Grams
We present a new index for approximate string matching. The index collects text q-samples, that is, disjoint text substrings of length q, at fixed intervals and stores their posi...
Gonzalo Navarro, Erkki Sutinen, Jani Tanninen, Jor...