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» Approximate algorithms for neural-Bayesian approaches
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SIAMCO
2002
71views more  SIAMCO 2002»
15 years 4 months ago
Rate of Convergence for Constrained Stochastic Approximation Algorithms
There is a large literature on the rate of convergence problem for general unconstrained stochastic approximations. Typically, one centers the iterate n about the limit point then...
Robert Buche, Harold J. Kushner
JMM2
2007
96views more  JMM2 2007»
15 years 4 months ago
A Framework for Linear Transform Approximation Using Orthogonal Basis Projection
—This paper aims to develop a novel framework to systematically trade-off computational complexity with output distortion in linear multimedia transforms, in an optimal manner. T...
Yinpeng Chen, Hari Sundaram
ICML
2010
IEEE
15 years 5 months ago
Feature Selection Using Regularization in Approximate Linear Programs for Markov Decision Processes
Approximate dynamic programming has been used successfully in a large variety of domains, but it relies on a small set of provided approximation features to calculate solutions re...
Marek Petrik, Gavin Taylor, Ronald Parr, Shlomo Zi...
TPDS
2008
120views more  TPDS 2008»
15 years 4 months ago
A New Storage Scheme for Approximate Location Queries in Object-Tracking Sensor Networks
Energy efficiency is one of the most critical issues in the design of wireless sensor networks. Observing that many sensor applications for object tracking can tolerate a certain d...
Jianliang Xu, Xueyan Tang, Wang-Chien Lee
CIKM
2003
Springer
15 years 9 months ago
Dimensionality reduction using magnitude and shape approximations
High dimensional data sets are encountered in many modern database applications. The usual approach is to construct a summary of the data set through a lossy compression technique...
Ümit Y. Ogras, Hakan Ferhatosmanoglu