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» Approximate algorithms for neural-Bayesian approaches
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SIAMCO
2002
71views more  SIAMCO 2002»
14 years 9 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»
14 years 9 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
14 years 10 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»
14 years 9 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
77
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CIKM
2003
Springer
15 years 3 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